With the rapid development of information technology,IoT devices play a huge role in physiological health data detection.The exponential growth of medical data requires us to reasonably allocate storage space for clou...With the rapid development of information technology,IoT devices play a huge role in physiological health data detection.The exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge nodes.The storage capacity of edge nodes close to users is limited.We should store hotspot data in edge nodes as much as possible,so as to ensure response timeliness and access hit rate;However,the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data;How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging problem.Our paper proposes a redundant data detection method that meets the privacy protection requirements.By scanning the cipher text,it is determined whether each sub-message of the data in the edge node meets the requirements of the hot data.It has the same effect as zero-knowledge proof,and it will not reveal the privacy of users.In addition,for redundant sub-data that does not meet the requirements of hot data,our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the data.We use Content Extraction Signature(CES)to generate the remaining hot data signature after the redundant data is deleted.The feasibility of the scheme is proved through safety analysis and efficiency analysis.展开更多
With the rapid development of the Internet of Things(IoT),Location-Based Services(LBS)are becoming more and more popular.However,for the users being served,how to protect their location privacy has become a growing co...With the rapid development of the Internet of Things(IoT),Location-Based Services(LBS)are becoming more and more popular.However,for the users being served,how to protect their location privacy has become a growing concern.This has led to great difficulty in establishing trust between the users and the service providers,hindering the development of LBS for more comprehensive functions.In this paper,we first establish a strong identity verification mechanism to ensure the authentication security of the system and then design a new location privacy protection mechanism based on the privacy proximity test problem.This mechanism not only guarantees the confidentiality of the user s information during the subsequent information interaction and dynamic data transmission,but also meets the service provider's requirements for related data.展开更多
Multi-Source data plays an important role in the evolution of media convergence.Its fusion processing enables the further mining of data and utilization of data value and broadens the path for the sharing and dissemin...Multi-Source data plays an important role in the evolution of media convergence.Its fusion processing enables the further mining of data and utilization of data value and broadens the path for the sharing and dissemination of media data.However,it also faces serious problems in terms of protecting user and data privacy.Many privacy protectionmethods have been proposed to solve the problemof privacy leakage during the process of data sharing,but they suffer fromtwo flaws:1)the lack of algorithmic frameworks for specific scenarios such as dynamic datasets in the media domain;2)the inability to solve the problem of the high computational complexity of ciphertext in multi-source data privacy protection,resulting in long encryption and decryption times.In this paper,we propose a multi-source data privacy protection method based on homomorphic encryption and blockchain technology,which solves the privacy protection problem ofmulti-source heterogeneous data in the dissemination ofmedia and reduces ciphertext processing time.We deployed the proposedmethod on theHyperledger platformfor testing and compared it with the privacy protection schemes based on k-anonymity and differential privacy.The experimental results showthat the key generation,encryption,and decryption times of the proposedmethod are lower than those in data privacy protection methods based on k-anonymity technology and differential privacy technology.This significantly reduces the processing time ofmulti-source data,which gives it potential for use in many applications.展开更多
With the increasing number of smart devices and the development of machine learning technology,the value of users’personal data is becoming more and more important.Based on the premise of protecting users’personal p...With the increasing number of smart devices and the development of machine learning technology,the value of users’personal data is becoming more and more important.Based on the premise of protecting users’personal privacy data,federated learning(FL)uses data stored on edge devices to realize training tasks by contributing training model parameters without revealing the original data.However,since FL can still leak the user’s original data by exchanging gradient information.The existing privacy protection strategy will increase the uplink time due to encryption measures.It is a huge challenge in terms of communication.When there are a large number of devices,the privacy protection cost of the system is higher.Based on these issues,we propose a privacy-preserving scheme of user-based group collaborative federated learning(GrCol-PPFL).Our scheme primarily divides participants into several groups and each group communicates in a chained transmission mechanism.All groups work in parallel at the same time.The server distributes a random parameter with the same dimension as the model parameter for each participant as a mask for the model parameter.We use the public datasets of modified national institute of standards and technology database(MNIST)to test the model accuracy.The experimental results show that GrCol-PPFL not only ensures the accuracy of themodel,but also ensures the security of the user’s original data when users collude with each other.Finally,through numerical experiments,we show that by changing the number of groups,we can find the optimal number of groups that reduces the uplink consumption time.展开更多
The car-hailing platform based on Internet of Vehicles(IoV)tech-nology greatly facilitates passengers’daily car-hailing,enabling drivers to obtain orders more efficiently and obtain more significant benefits.However,...The car-hailing platform based on Internet of Vehicles(IoV)tech-nology greatly facilitates passengers’daily car-hailing,enabling drivers to obtain orders more efficiently and obtain more significant benefits.However,to match the driver closest to the passenger,it is often necessary to process the location information of the passenger and driver,which poses a considerable threat to privacy disclosure to the passenger and driver.Targeting these issues,in this paper,by combining blockchain and Paillier homomorphic encryption algorithm,we design a secure blockchain-enabled IoV scheme with privacy protection for online car-hailing.In this scheme,firstly,we propose an encryp-tion scheme based on the lattice.Thus,the location information of passengers and drivers is encrypted in this system.Secondly,by introducing Paillier homomorphic encryption algorithm,the location matching of passengers and drivers is carried out in the ciphertext state to protect their location privacy.At last,blockchain technology is used to record the transactions in online car-hailing,which can provide a security guarantee for passengers and drivers.And we further analyze the security and performance of this scheme.Compared with other schemes,the experimental results show that the proposed scheme can protect the user’s location privacy and have a better performance.展开更多
With the rapid increase in demand for data trustworthiness and data security,distributed data storage technology represented by blockchain has received unprecedented attention.These technologies have been suggested fo...With the rapid increase in demand for data trustworthiness and data security,distributed data storage technology represented by blockchain has received unprecedented attention.These technologies have been suggested for various uses because of their remarkable ability to offer decentralization,high autonomy,full process traceability,and tamper resistance.Blockchain enables the exchange of information and value in an untrusted environment.There has been a significant increase in attention to the confidentiality and privacy preservation of blockchain technology.Ensuring data privacy is a critical concern in cryptography,and one of the most important protocols used to achieve this is the secret-sharing method.By dividing the secret into shares and distributing them among multiple parties,no one can access the secret without the cooperation of the other parties.However,Attackers with quantum computers in the future can execute Grover’s and Shor’s algorithms on quantum computers that can break or reduce the currently widely used cryptosystems.Furthermore,centralized management of keys increases the risk of key leakage.This paper proposed a post-quantum threshold algo-rithm to reduce the risk of data privacy leakage in blockchain Systems.This algorithm uses distributed key management technology to reduce the risk of individual node private key leakage and provide post-quantum security.The proposed privacy-preserving cryptographic algorithm provides a post-quantum threshold architecture for managing data,which involves defining users and interaction processes within the system.This paper applies a linear secret-sharing solution to partition the private key of the Number Theory Research Unit(NTRU)algorithm into n parts.It constructs a t–n threshold that allows recovery of the plaintext only when more than t nodes participate in decryption.The characteristic of a threshold makes the scheme resistant to collusion attacks from members whose combined credibility is less than the threshold.This mitigates the risk of single-point private key leakage.During the threshold decryption process,the private key information of the nodes will not be leaked.In addition,the fact that the threshold algorithm is founded on the NTRU lattice enables it to withstand quantum attacks,thus enhancing its security.According to the analysis,the proposed scheme provides superior protection compared to currently availablemethods.This paper provides postquantum security solutions for data security protection of blockchain,which will enrich the use of blockchain in scenarios with strict requirements for data privacy protection.展开更多
Most studies have conducted experiments on predicting energy consumption by integrating data formodel training.However, the process of centralizing data can cause problems of data leakage.Meanwhile,many laws and regul...Most studies have conducted experiments on predicting energy consumption by integrating data formodel training.However, the process of centralizing data can cause problems of data leakage.Meanwhile,many laws and regulationson data security and privacy have been enacted, making it difficult to centralize data, which can lead to a datasilo problem. Thus, to train the model while maintaining user privacy, we adopt a federated learning framework.However, in all classical federated learning frameworks secure aggregation, the Federated Averaging (FedAvg)method is used to directly weight the model parameters on average, which may have an adverse effect on te model.Therefore, we propose the Federated Reinforcement Learning (FedRL) model, which consists of multiple userscollaboratively training the model. Each household trains a local model on local data. These local data neverleave the local area, and only the encrypted parameters are uploaded to the central server to participate in thesecure aggregation of the global model. We improve FedAvg by incorporating a Q-learning algorithm to assignweights to each locally uploaded local model. And the model has improved predictive performance. We validatethe performance of the FedRL model by testing it on a real-world dataset and compare the experimental results withother models. The performance of our proposed method in most of the evaluation metrics is improved comparedto both the centralized and distributed models.展开更多
In edge computing,a reasonable edge resource bidding mechanism can enable edge providers and users to obtain benefits in a relatively fair fashion.To maximize such benefits,this paper proposes a dynamic multiattribute...In edge computing,a reasonable edge resource bidding mechanism can enable edge providers and users to obtain benefits in a relatively fair fashion.To maximize such benefits,this paper proposes a dynamic multiattribute resource bidding mechanism(DMRBM).Most of the previous work mainly relies on a third-party agent to exchange information to gain optimal benefits.It isworth noting thatwhen edge providers and users trade with thirdparty agents which are not entirely reliable and trustworthy,their sensitive information is prone to be leaked.Moreover,the privacy protection of edge providers and users must be considered in the dynamic pricing/transaction process,which is also very challenging.Therefore,this paper first adopts a privacy protection algorithm to prevent sensitive information from leakage.On the premise that the sensitive data of both edge providers and users are protected,the prices of providers fluctuate within a certain range.Then,users can choose appropriate edge providers by the price-performance ratio(PPR)standard and the reward of lower price(LPR)standard according to their demands.The two standards can be evolved by two evaluation functions.Furthermore,this paper employs an approximate computing method to get an approximate solution of DMRBM in polynomial time.Specifically,this paper models the bidding process as a non-cooperative game and obtains the approximate optimal solution based on two standards according to the game theory.Through the extensive experiments,this paper demonstrates that the DMRBM satisfies the individual rationality,budget balance,and privacy protection and it can also increase the task offloading rate and the system benefits.展开更多
In recent years,the issue of preserving the privacy of parties involved in blockchain transactions has garnered significant attention.To ensure privacy protection for both sides of the transaction,many researchers are...In recent years,the issue of preserving the privacy of parties involved in blockchain transactions has garnered significant attention.To ensure privacy protection for both sides of the transaction,many researchers are using ring signature technology instead of the original signature technology.However,in practice,identifying the signer of an illegal blockchain transaction once it has been placed on the chain necessitates a signature technique that offers conditional anonymity.Some illegals can conduct illegal transactions and evade the lawusing ring signatures,which offer perfect anonymity.This paper firstly constructs a conditionally anonymous linkable ring signature using the Diffie-Hellman key exchange protocol and the Elliptic Curve Discrete Logarithm,which offers a non-interactive process for finding the signer of a ring signature in a specific case.Secondly,this paper’s proposed scheme is proven correct and secure under Elliptic Curve Discrete Logarithm Assumptions.Lastly,compared to previous constructions,the scheme presented in this paper provides a non-interactive,efficient,and secure confirmation process.In addition,this paper presents the implementation of the proposed scheme on a personal computer,where the confirmation process takes only 2,16,and 24ms for ring sizes of 4,24 and 48,respectively,and the confirmation process can be combined with a smart contract on the blockchain with a tested millisecond level of running efficiency.In conclusion,the proposed scheme offers a solution to the challenge of identifying the signer of an illegal blockchain transaction,making it an essential contribution to the field.展开更多
In recent years,the type and quantity of news are growing rapidly,and it is not easy for users to find the news they are interested in the massive amount of news.A news recommendation system can score and predict the ...In recent years,the type and quantity of news are growing rapidly,and it is not easy for users to find the news they are interested in the massive amount of news.A news recommendation system can score and predict the candidate news,and finally recommend the news with high scores to users.However,existing user models usually only consider users’long-term interests and ignore users’recent interests,which affects users’usage experience.Therefore,this paper introduces gated recurrent unit(GRU)sequence network to capture users’short-term interests and combines users’short-term interests and long-terminterests to characterize users.While existing models often only use the user’s browsing history and ignore the variability of different users’interest in the same news,we introduce additional user’s ID information and apply the personalized attention mechanism for user representation.Thus,we achieve a more accurate user representation.We also consider the risk of compromising user privacy if the user model training is placed on the server side.To solve this problem,we design the training of the user model locally on the client side by introducing a federated learning framework to keep the user’s browsing history on the client side.We further employ secure multiparty computation to request news representations from the server side,which protects privacy to some extent.Extensive experiments on a real-world news dataset show that our proposed news recommendation model has a better improvement in several performance evaluation metrics.Compared with the current state-of-the-art federated news recommendation models,our model has increased by 0.54%in AUC,1.97%in MRR,2.59%in nDCG@5%,and 1.89%in nDCG@10.At the same time,because we use a federated learning framework,compared with other centralized news recommendation methods,we achieve privacy protection for users.展开更多
As Vehicular ad hoc networks (VANETs) become more sophisticated, the importance of integrating data protection and cybersecurity is increasingly evident. This paper offers a comprehensive investigation into the challe...As Vehicular ad hoc networks (VANETs) become more sophisticated, the importance of integrating data protection and cybersecurity is increasingly evident. This paper offers a comprehensive investigation into the challenges and solutions associated with the privacy implications within VANETs, rooted in an intricate landscape of cross-jurisdictional data protection regulations. Our examination underscores the unique nature of VANETs, which, unlike other ad-hoc networks, demand heightened security and privacy considerations due to their exposure to sensitive data such as vehicle identifiers, routes, and more. Through a rigorous exploration of pseudonymization schemes, with a notable emphasis on the Density-based Location Privacy (DLP) method, we elucidate the potential to mitigate and sometimes sidestep the heavy compliance burdens associated with data protection laws. Furthermore, this paper illuminates the cybersecurity vulnerabilities inherent to VANETs, proposing robust countermeasures, including secure data transmission protocols. In synthesizing our findings, we advocate for the proactive adoption of protective mechanisms to facilitate the broader acceptance of VANET technology while concurrently addressing regulatory and cybersecurity hurdles.展开更多
The Personal Information Protection Law,as the first law on personal information protection in China,hits the people’s most concerned,realistic and direct privacy and information security issues,and plays an extremel...The Personal Information Protection Law,as the first law on personal information protection in China,hits the people’s most concerned,realistic and direct privacy and information security issues,and plays an extremely important role in promoting the development of the digital economy,the legalization of socialism with Chinese characteristics and social public security,and marks a new historical development stage in the protection of personal information in China.However,the awareness of privacy protection and privacy protection behavior of the public in personal information privacy protection is weak.Based on the literature review and in-depth understanding of current legal regulations,this study integrates the relevant literature and theoretical knowledge of the Personal Protection Law to construct a conceptual model of“privacy information protection willingness-privacy information protection behavior”.Taking the residents of Foshan City as an example,this paper conducts a questionnaire survey on their attitudes toward the Personal Protection Law,analyzes the factors influencing their willingness to protect their privacy and their behaviors,and explores the mechanisms of their influencing variables,to provide advice and suggestions for promoting the protection of privacy information and building a security barrier for the high-quality development of public information security.展开更多
Federated learning is a new type of distributed learning framework that allows multiple participants to share training results without revealing their data privacy.As data privacy becomes more important,it becomes dif...Federated learning is a new type of distributed learning framework that allows multiple participants to share training results without revealing their data privacy.As data privacy becomes more important,it becomes difficult to collect data from multiple data owners to make machine learning predictions due to the lack of data security.Data is forced to be stored independently between companies,creating“data silos”.With the goal of safeguarding data privacy and security,the federated learning framework greatly expands the amount of training data,effectively improving the shortcomings of traditional machine learning and deep learning,and bringing AI algorithms closer to our reality.In the context of the current international data security issues,federated learning is developing rapidly and has gradually moved from the theoretical to the applied level.The paper first introduces the federated learning framework,analyzes its advantages,reviews the results of federated learning applications in industries such as communication and healthcare,then analyzes the pitfalls of federated learning and discusses the security issues that should be considered in applications,and finally looks into the future of federated learning and the application layer.展开更多
Wireless transmission method in wireless sensor networks has put forward higher requirements for private protection technology. According to the packet loss problem of private protection algorithm based on slice techn...Wireless transmission method in wireless sensor networks has put forward higher requirements for private protection technology. According to the packet loss problem of private protection algorithm based on slice technology, this paper proposes the data private protection algorithm with redundancy mechanism, which ensures privacy by privacy homomorphism mechanism and guarantees redundancy by carrying hidden data. Moreover,it selects the routing tree generated by CTP(Collection Tree Protocol) as routing path for data transmission. By dividing at the source node, it adds the hidden information and also the privacy homomorphism. At the same time,the information feedback tree is established between the destination node and the source node. In addition, the destination node immediately sends the packet loss information and the encryption key via the information feedback tree to the source node. As a result,it improves the reliability and privacy of data transmission and ensures the data redundancy.展开更多
The leakage of medical audio data in telemedicine seriously violates the privacy of patients.In order to avoid the leakage of patient information in telemedicine,a two-stage reversible robust audio watermarking algori...The leakage of medical audio data in telemedicine seriously violates the privacy of patients.In order to avoid the leakage of patient information in telemedicine,a two-stage reversible robust audio watermarking algorithm is proposed to protect medical audio data.The scheme decomposes the medical audio into two independent embedding domains,embeds the robust watermark and the reversible watermark into the two domains respectively.In order to ensure the audio quality,the Hurst exponent is used to find a suitable position for watermark embedding.Due to the independence of the two embedding domains,the embedding of the second-stage reversible watermark will not affect the first-stage watermark,so the robustness of the first-stage watermark can be well maintained.In the second stage,the correlation between the sampling points in the medical audio is used to modify the hidden bits of the histogram to reduce the modification of the medical audio and reduce the distortion caused by reversible embedding.Simulation experiments show that this scheme has strong robustness against signal processing operations such as MP3 compression of 48 db,additive white Gaussian noise(AWGN)of 20 db,low-pass filtering,resampling,re-quantization and other attacks,and has good imperceptibility.展开更多
The blockchain technology has been applied to wide areas.However,the open and transparent properties of the blockchains pose serious challenges to users’privacy.Among all the schemes for the privacy protection,the ze...The blockchain technology has been applied to wide areas.However,the open and transparent properties of the blockchains pose serious challenges to users’privacy.Among all the schemes for the privacy protection,the zero-knowledge proof algorithm conceals most of the private information in a transaction,while participants of the blockchain can validate this transaction without the private information.However,current schemes are only aimed at blockchains with the UTXO model,and only one type of assets circulates on these blockchains.Based on the zero-knowledge proof algorithm,this paper proposes a privacy protection scheme for blockchains that use the account and multi-asset model.We design the transaction structure,anonymous addresses and anonymous asset metadata,and also propose the methods of the asset transfer and double-spending detection.The zk-SNARKs algorithm is used to generate and to verify the zero-knowledge proof.And finally,we conduct the experiments to evaluate our scheme.展开更多
With the rapid development of computer technology, cloud-based services have become a hot topic. They not only provide users with convenience, but also bring many security issues, such as data sharing and privacy issu...With the rapid development of computer technology, cloud-based services have become a hot topic. They not only provide users with convenience, but also bring many security issues, such as data sharing and privacy issue. In this paper, we present an access control system with privilege separation based on privacy protection(PS-ACS). In the PS-ACS scheme, we divide users into private domain(PRD) and public domain(PUD) logically. In PRD, to achieve read access permission and write access permission, we adopt the Key-Aggregate Encryption(KAE) and the Improved Attribute-based Signature(IABS) respectively. In PUD, we construct a new multi-authority ciphertext policy attribute-based encryption(CP-ABE) scheme with efficient decryption to avoid the issues of single point of failure and complicated key distribution, and design an efficient attribute revocation method for it. The analysis and simulation result show that our scheme is feasible and superior to protect users' privacy in cloud-based services.展开更多
In recent years,with the continuous advancement of the intelligent process of the Internet of Vehicles(IoV),the problem of privacy leakage in IoV has become increasingly prominent.The research on the privacy protectio...In recent years,with the continuous advancement of the intelligent process of the Internet of Vehicles(IoV),the problem of privacy leakage in IoV has become increasingly prominent.The research on the privacy protection of the IoV has become the focus of the society.This paper analyzes the advantages and disadvantages of the existing location privacy protection system structure and algorithms,proposes a privacy protection system structure based on untrusted data collection server,and designs a vehicle location acquisition algorithm based on a local differential privacy and game model.The algorithm first meshes the road network space.Then,the dynamic game model is introduced into the game user location privacy protection model and the attacker location semantic inference model,thereby minimizing the possibility of exposing the regional semantic privacy of the k-location set while maximizing the availability of the service.On this basis,a statistical method is designed,which satisfies the local differential privacy of k-location sets and obtains unbiased estimation of traffic density in different regions.Finally,this paper verifies the algorithm based on the data set of mobile vehicles in Shanghai.The experimental results show that the algorithm can guarantee the user’s location privacy and location semantic privacy while satisfying the service quality requirements,and provide better privacy protection and service for the users of the IoV.展开更多
The problem of data island hinders the application of big data in artificial intelligence model training,so researchers propose a federated learning framework.It enables model training without having to centralize all...The problem of data island hinders the application of big data in artificial intelligence model training,so researchers propose a federated learning framework.It enables model training without having to centralize all data in a central storage point.In the current horizontal federated learning scheme,each participant gets the final jointly trained model.No solution is proposed for scenarios where participants only provide training data in exchange for benefits,but do not care about the final jointly trained model.Therefore,this paper proposes a newboosted tree algorithm,calledRPBT(the originator Rights Protected federated Boosted Tree algorithm).Compared with the current horizontal federal learning algorithm,each participant will obtain the final jointly trained model.RPBT can guarantee that the local data of the participants will not be leaked,while the final jointly trained model cannot be obtained.It is worth mentioning that,from the perspective of the participants,the scheme uses the batch idea to make the participants participate in the training in random batches.Therefore,this scheme is more suitable for scenarios where a large number of participants are jointly modeling.Furthermore,a small number of participants will not actually participate in the joint training process.Therefore,the proposed scheme is more secure.Theoretical analysis and experimental evaluations show that RPBT is secure,accurate and efficient.展开更多
A comprehensive analysis of the impact privacy incidents on its market value is given.A broad set of instances of the exposure of personal information from a summary of some security mechanisms and the corresponding r...A comprehensive analysis of the impact privacy incidents on its market value is given.A broad set of instances of the exposure of personal information from a summary of some security mechanisms and the corresponding results are presented. The cumulative effect increases in magnitude over day following the breach announcement, but then decreases. Besides, a new privacy protection property, that is, p-sensitive k-anonymity is presented in this paper to protect against identity disclosure. We illustrated the inclusion of the two necessary conditions in the algorithm for computing a p-k-minimal generalization. Algorithms such as k-anonymity and l-diversity remain all sensitive attributes intact and apply generalization and suppression to the quasi-identifiers. This will keep the data "truthful" and provide good utility for data-mining applications, while achieving less perfect privacy. We aim to get the problem based on the prior analysis, and study the issue of privacy protection from the perspective of the model-benefit.展开更多
基金sponsored by the National Natural Science Foundation of China under grant number No. 62172353, No. 62302114, No. U20B2046 and No. 62172115Innovation Fund Program of the Engineering Research Center for Integration and Application of Digital Learning Technology of Ministry of Education No.1331007 and No. 1311022+1 种基金Natural Science Foundation of the Jiangsu Higher Education Institutions Grant No. 17KJB520044Six Talent Peaks Project in Jiangsu Province No.XYDXX-108
文摘With the rapid development of information technology,IoT devices play a huge role in physiological health data detection.The exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge nodes.The storage capacity of edge nodes close to users is limited.We should store hotspot data in edge nodes as much as possible,so as to ensure response timeliness and access hit rate;However,the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data;How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging problem.Our paper proposes a redundant data detection method that meets the privacy protection requirements.By scanning the cipher text,it is determined whether each sub-message of the data in the edge node meets the requirements of the hot data.It has the same effect as zero-knowledge proof,and it will not reveal the privacy of users.In addition,for redundant sub-data that does not meet the requirements of hot data,our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the data.We use Content Extraction Signature(CES)to generate the remaining hot data signature after the redundant data is deleted.The feasibility of the scheme is proved through safety analysis and efficiency analysis.
基金This work has been partly supported by the National Natural Science Foundation of China under Grant No.61702212the Fundamental Research Funds for the Central Universities under Grand NO.CCNU19TS017.
文摘With the rapid development of the Internet of Things(IoT),Location-Based Services(LBS)are becoming more and more popular.However,for the users being served,how to protect their location privacy has become a growing concern.This has led to great difficulty in establishing trust between the users and the service providers,hindering the development of LBS for more comprehensive functions.In this paper,we first establish a strong identity verification mechanism to ensure the authentication security of the system and then design a new location privacy protection mechanism based on the privacy proximity test problem.This mechanism not only guarantees the confidentiality of the user s information during the subsequent information interaction and dynamic data transmission,but also meets the service provider's requirements for related data.
基金funded by the High-Quality and Cutting-Edge Discipline Construction Project for Universities in Beijing (Internet Information,Communication University of China).
文摘Multi-Source data plays an important role in the evolution of media convergence.Its fusion processing enables the further mining of data and utilization of data value and broadens the path for the sharing and dissemination of media data.However,it also faces serious problems in terms of protecting user and data privacy.Many privacy protectionmethods have been proposed to solve the problemof privacy leakage during the process of data sharing,but they suffer fromtwo flaws:1)the lack of algorithmic frameworks for specific scenarios such as dynamic datasets in the media domain;2)the inability to solve the problem of the high computational complexity of ciphertext in multi-source data privacy protection,resulting in long encryption and decryption times.In this paper,we propose a multi-source data privacy protection method based on homomorphic encryption and blockchain technology,which solves the privacy protection problem ofmulti-source heterogeneous data in the dissemination ofmedia and reduces ciphertext processing time.We deployed the proposedmethod on theHyperledger platformfor testing and compared it with the privacy protection schemes based on k-anonymity and differential privacy.The experimental results showthat the key generation,encryption,and decryption times of the proposedmethod are lower than those in data privacy protection methods based on k-anonymity technology and differential privacy technology.This significantly reduces the processing time ofmulti-source data,which gives it potential for use in many applications.
基金supported by the Major science and technology project of Hainan Province(Grant No.ZDKJ2020012)National Natural Science Foundation of China(Grant No.62162024 and 62162022)Key Projects in Hainan Province(Grant ZDYF2021GXJS003 and Grant ZDYF2020040).
文摘With the increasing number of smart devices and the development of machine learning technology,the value of users’personal data is becoming more and more important.Based on the premise of protecting users’personal privacy data,federated learning(FL)uses data stored on edge devices to realize training tasks by contributing training model parameters without revealing the original data.However,since FL can still leak the user’s original data by exchanging gradient information.The existing privacy protection strategy will increase the uplink time due to encryption measures.It is a huge challenge in terms of communication.When there are a large number of devices,the privacy protection cost of the system is higher.Based on these issues,we propose a privacy-preserving scheme of user-based group collaborative federated learning(GrCol-PPFL).Our scheme primarily divides participants into several groups and each group communicates in a chained transmission mechanism.All groups work in parallel at the same time.The server distributes a random parameter with the same dimension as the model parameter for each participant as a mask for the model parameter.We use the public datasets of modified national institute of standards and technology database(MNIST)to test the model accuracy.The experimental results show that GrCol-PPFL not only ensures the accuracy of themodel,but also ensures the security of the user’s original data when users collude with each other.Finally,through numerical experiments,we show that by changing the number of groups,we can find the optimal number of groups that reduces the uplink consumption time.
基金supported by National Key R&D Program of China(Grant No.2020YFB1805403)Major Scientific and Technological Special Project of Guizhou Province(Grant No.20183001)+1 种基金Foundation of Guizhou Provincial Key Laboratory of Public Big Data(Grant Nos.2018BDKFJJ021,2018BDKFJJ020,2017BDKFJJ015,2018BDKFJJ008)the Fundamental Research Funds for the Central Universities(CUC210A003).
文摘The car-hailing platform based on Internet of Vehicles(IoV)tech-nology greatly facilitates passengers’daily car-hailing,enabling drivers to obtain orders more efficiently and obtain more significant benefits.However,to match the driver closest to the passenger,it is often necessary to process the location information of the passenger and driver,which poses a considerable threat to privacy disclosure to the passenger and driver.Targeting these issues,in this paper,by combining blockchain and Paillier homomorphic encryption algorithm,we design a secure blockchain-enabled IoV scheme with privacy protection for online car-hailing.In this scheme,firstly,we propose an encryp-tion scheme based on the lattice.Thus,the location information of passengers and drivers is encrypted in this system.Secondly,by introducing Paillier homomorphic encryption algorithm,the location matching of passengers and drivers is carried out in the ciphertext state to protect their location privacy.At last,blockchain technology is used to record the transactions in online car-hailing,which can provide a security guarantee for passengers and drivers.And we further analyze the security and performance of this scheme.Compared with other schemes,the experimental results show that the proposed scheme can protect the user’s location privacy and have a better performance.
基金supported by the National Key R&D Program of China(2022YFB2703400).
文摘With the rapid increase in demand for data trustworthiness and data security,distributed data storage technology represented by blockchain has received unprecedented attention.These technologies have been suggested for various uses because of their remarkable ability to offer decentralization,high autonomy,full process traceability,and tamper resistance.Blockchain enables the exchange of information and value in an untrusted environment.There has been a significant increase in attention to the confidentiality and privacy preservation of blockchain technology.Ensuring data privacy is a critical concern in cryptography,and one of the most important protocols used to achieve this is the secret-sharing method.By dividing the secret into shares and distributing them among multiple parties,no one can access the secret without the cooperation of the other parties.However,Attackers with quantum computers in the future can execute Grover’s and Shor’s algorithms on quantum computers that can break or reduce the currently widely used cryptosystems.Furthermore,centralized management of keys increases the risk of key leakage.This paper proposed a post-quantum threshold algo-rithm to reduce the risk of data privacy leakage in blockchain Systems.This algorithm uses distributed key management technology to reduce the risk of individual node private key leakage and provide post-quantum security.The proposed privacy-preserving cryptographic algorithm provides a post-quantum threshold architecture for managing data,which involves defining users and interaction processes within the system.This paper applies a linear secret-sharing solution to partition the private key of the Number Theory Research Unit(NTRU)algorithm into n parts.It constructs a t–n threshold that allows recovery of the plaintext only when more than t nodes participate in decryption.The characteristic of a threshold makes the scheme resistant to collusion attacks from members whose combined credibility is less than the threshold.This mitigates the risk of single-point private key leakage.During the threshold decryption process,the private key information of the nodes will not be leaked.In addition,the fact that the threshold algorithm is founded on the NTRU lattice enables it to withstand quantum attacks,thus enhancing its security.According to the analysis,the proposed scheme provides superior protection compared to currently availablemethods.This paper provides postquantum security solutions for data security protection of blockchain,which will enrich the use of blockchain in scenarios with strict requirements for data privacy protection.
基金supported by National Key R&D Program of China(No.2020YFC2006602)National Natural Science Foundation of China(Nos.62172324,62072324,61876217,6187612)+2 种基金University Natural Science Foundation of Jiangsu Province(No.21KJA520005)Primary Research and Development Plan of Jiangsu Province(No.BE2020026)Natural Science Foundation of Jiangsu Province(No.BK20190942).
文摘Most studies have conducted experiments on predicting energy consumption by integrating data formodel training.However, the process of centralizing data can cause problems of data leakage.Meanwhile,many laws and regulationson data security and privacy have been enacted, making it difficult to centralize data, which can lead to a datasilo problem. Thus, to train the model while maintaining user privacy, we adopt a federated learning framework.However, in all classical federated learning frameworks secure aggregation, the Federated Averaging (FedAvg)method is used to directly weight the model parameters on average, which may have an adverse effect on te model.Therefore, we propose the Federated Reinforcement Learning (FedRL) model, which consists of multiple userscollaboratively training the model. Each household trains a local model on local data. These local data neverleave the local area, and only the encrypted parameters are uploaded to the central server to participate in thesecure aggregation of the global model. We improve FedAvg by incorporating a Q-learning algorithm to assignweights to each locally uploaded local model. And the model has improved predictive performance. We validatethe performance of the FedRL model by testing it on a real-world dataset and compare the experimental results withother models. The performance of our proposed method in most of the evaluation metrics is improved comparedto both the centralized and distributed models.
基金supported in part by National Natural Science Foundation of China under Grant No.62172349,62032020,and 62172350the Research Foundation of Education Bureau of Hunan Province under Grant No.21B0139+1 种基金the National Key Research and Development Program of China under Grant 2021YFB3101200Hunan Science and Technology Planning Project under Grant No.2019RS3019.
文摘In edge computing,a reasonable edge resource bidding mechanism can enable edge providers and users to obtain benefits in a relatively fair fashion.To maximize such benefits,this paper proposes a dynamic multiattribute resource bidding mechanism(DMRBM).Most of the previous work mainly relies on a third-party agent to exchange information to gain optimal benefits.It isworth noting thatwhen edge providers and users trade with thirdparty agents which are not entirely reliable and trustworthy,their sensitive information is prone to be leaked.Moreover,the privacy protection of edge providers and users must be considered in the dynamic pricing/transaction process,which is also very challenging.Therefore,this paper first adopts a privacy protection algorithm to prevent sensitive information from leakage.On the premise that the sensitive data of both edge providers and users are protected,the prices of providers fluctuate within a certain range.Then,users can choose appropriate edge providers by the price-performance ratio(PPR)standard and the reward of lower price(LPR)standard according to their demands.The two standards can be evolved by two evaluation functions.Furthermore,this paper employs an approximate computing method to get an approximate solution of DMRBM in polynomial time.Specifically,this paper models the bidding process as a non-cooperative game and obtains the approximate optimal solution based on two standards according to the game theory.Through the extensive experiments,this paper demonstrates that the DMRBM satisfies the individual rationality,budget balance,and privacy protection and it can also increase the task offloading rate and the system benefits.
基金funded by the National Natural Science Foundation of China (Grant Number 12171114)National Key R&D Program of China (Grant Number 2021YFA1000600).
文摘In recent years,the issue of preserving the privacy of parties involved in blockchain transactions has garnered significant attention.To ensure privacy protection for both sides of the transaction,many researchers are using ring signature technology instead of the original signature technology.However,in practice,identifying the signer of an illegal blockchain transaction once it has been placed on the chain necessitates a signature technique that offers conditional anonymity.Some illegals can conduct illegal transactions and evade the lawusing ring signatures,which offer perfect anonymity.This paper firstly constructs a conditionally anonymous linkable ring signature using the Diffie-Hellman key exchange protocol and the Elliptic Curve Discrete Logarithm,which offers a non-interactive process for finding the signer of a ring signature in a specific case.Secondly,this paper’s proposed scheme is proven correct and secure under Elliptic Curve Discrete Logarithm Assumptions.Lastly,compared to previous constructions,the scheme presented in this paper provides a non-interactive,efficient,and secure confirmation process.In addition,this paper presents the implementation of the proposed scheme on a personal computer,where the confirmation process takes only 2,16,and 24ms for ring sizes of 4,24 and 48,respectively,and the confirmation process can be combined with a smart contract on the blockchain with a tested millisecond level of running efficiency.In conclusion,the proposed scheme offers a solution to the challenge of identifying the signer of an illegal blockchain transaction,making it an essential contribution to the field.
文摘In recent years,the type and quantity of news are growing rapidly,and it is not easy for users to find the news they are interested in the massive amount of news.A news recommendation system can score and predict the candidate news,and finally recommend the news with high scores to users.However,existing user models usually only consider users’long-term interests and ignore users’recent interests,which affects users’usage experience.Therefore,this paper introduces gated recurrent unit(GRU)sequence network to capture users’short-term interests and combines users’short-term interests and long-terminterests to characterize users.While existing models often only use the user’s browsing history and ignore the variability of different users’interest in the same news,we introduce additional user’s ID information and apply the personalized attention mechanism for user representation.Thus,we achieve a more accurate user representation.We also consider the risk of compromising user privacy if the user model training is placed on the server side.To solve this problem,we design the training of the user model locally on the client side by introducing a federated learning framework to keep the user’s browsing history on the client side.We further employ secure multiparty computation to request news representations from the server side,which protects privacy to some extent.Extensive experiments on a real-world news dataset show that our proposed news recommendation model has a better improvement in several performance evaluation metrics.Compared with the current state-of-the-art federated news recommendation models,our model has increased by 0.54%in AUC,1.97%in MRR,2.59%in nDCG@5%,and 1.89%in nDCG@10.At the same time,because we use a federated learning framework,compared with other centralized news recommendation methods,we achieve privacy protection for users.
文摘As Vehicular ad hoc networks (VANETs) become more sophisticated, the importance of integrating data protection and cybersecurity is increasingly evident. This paper offers a comprehensive investigation into the challenges and solutions associated with the privacy implications within VANETs, rooted in an intricate landscape of cross-jurisdictional data protection regulations. Our examination underscores the unique nature of VANETs, which, unlike other ad-hoc networks, demand heightened security and privacy considerations due to their exposure to sensitive data such as vehicle identifiers, routes, and more. Through a rigorous exploration of pseudonymization schemes, with a notable emphasis on the Density-based Location Privacy (DLP) method, we elucidate the potential to mitigate and sometimes sidestep the heavy compliance burdens associated with data protection laws. Furthermore, this paper illuminates the cybersecurity vulnerabilities inherent to VANETs, proposing robust countermeasures, including secure data transmission protocols. In synthesizing our findings, we advocate for the proactive adoption of protective mechanisms to facilitate the broader acceptance of VANET technology while concurrently addressing regulatory and cybersecurity hurdles.
文摘The Personal Information Protection Law,as the first law on personal information protection in China,hits the people’s most concerned,realistic and direct privacy and information security issues,and plays an extremely important role in promoting the development of the digital economy,the legalization of socialism with Chinese characteristics and social public security,and marks a new historical development stage in the protection of personal information in China.However,the awareness of privacy protection and privacy protection behavior of the public in personal information privacy protection is weak.Based on the literature review and in-depth understanding of current legal regulations,this study integrates the relevant literature and theoretical knowledge of the Personal Protection Law to construct a conceptual model of“privacy information protection willingness-privacy information protection behavior”.Taking the residents of Foshan City as an example,this paper conducts a questionnaire survey on their attitudes toward the Personal Protection Law,analyzes the factors influencing their willingness to protect their privacy and their behaviors,and explores the mechanisms of their influencing variables,to provide advice and suggestions for promoting the protection of privacy information and building a security barrier for the high-quality development of public information security.
基金supported by National Natural Science Foundation of China (NO.51974131)Hebei Province Natural Science Fund for Distinguished Young Scholars (NO.E2020209082).
文摘Federated learning is a new type of distributed learning framework that allows multiple participants to share training results without revealing their data privacy.As data privacy becomes more important,it becomes difficult to collect data from multiple data owners to make machine learning predictions due to the lack of data security.Data is forced to be stored independently between companies,creating“data silos”.With the goal of safeguarding data privacy and security,the federated learning framework greatly expands the amount of training data,effectively improving the shortcomings of traditional machine learning and deep learning,and bringing AI algorithms closer to our reality.In the context of the current international data security issues,federated learning is developing rapidly and has gradually moved from the theoretical to the applied level.The paper first introduces the federated learning framework,analyzes its advantages,reviews the results of federated learning applications in industries such as communication and healthcare,then analyzes the pitfalls of federated learning and discusses the security issues that should be considered in applications,and finally looks into the future of federated learning and the application layer.
基金sponsored by the National Key R&D Program of China(No.2018YFB1003201)the National Natural Science Foundation of China(No.61672296,No.61602261)Major Natural Science Research Projects in Colleges and Universities of Jiangsu Province(No.18KJA520008)
文摘Wireless transmission method in wireless sensor networks has put forward higher requirements for private protection technology. According to the packet loss problem of private protection algorithm based on slice technology, this paper proposes the data private protection algorithm with redundancy mechanism, which ensures privacy by privacy homomorphism mechanism and guarantees redundancy by carrying hidden data. Moreover,it selects the routing tree generated by CTP(Collection Tree Protocol) as routing path for data transmission. By dividing at the source node, it adds the hidden information and also the privacy homomorphism. At the same time,the information feedback tree is established between the destination node and the source node. In addition, the destination node immediately sends the packet loss information and the encryption key via the information feedback tree to the source node. As a result,it improves the reliability and privacy of data transmission and ensures the data redundancy.
基金This work was supported,in part,by the Natural Science Foundation of Jiangsu Province under Grant Numbers BK20201136,BK20191401in part,by the National Nature Science Foundation of China under Grant Numbers 61502240,61502096,61304205,61773219in part,by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fund.Conflicts of Interest:The aut。
文摘The leakage of medical audio data in telemedicine seriously violates the privacy of patients.In order to avoid the leakage of patient information in telemedicine,a two-stage reversible robust audio watermarking algorithm is proposed to protect medical audio data.The scheme decomposes the medical audio into two independent embedding domains,embeds the robust watermark and the reversible watermark into the two domains respectively.In order to ensure the audio quality,the Hurst exponent is used to find a suitable position for watermark embedding.Due to the independence of the two embedding domains,the embedding of the second-stage reversible watermark will not affect the first-stage watermark,so the robustness of the first-stage watermark can be well maintained.In the second stage,the correlation between the sampling points in the medical audio is used to modify the hidden bits of the histogram to reduce the modification of the medical audio and reduce the distortion caused by reversible embedding.Simulation experiments show that this scheme has strong robustness against signal processing operations such as MP3 compression of 48 db,additive white Gaussian noise(AWGN)of 20 db,low-pass filtering,resampling,re-quantization and other attacks,and has good imperceptibility.
基金supported by National Natural Science Foundation of China(61672499,61772502)Key Special Project of Beijing Municipal Science&Technology Commission(Z181100003218018)+1 种基金Natural Science Foundation of Inner Mongolia,Open Foundation of State key Laboratory of Networking and Switching Technology(Beijing University of Posts and Telecommunications,SKLNST-2016-2-09)SV-ICT Blockchain&DAPP Joint Lab
文摘The blockchain technology has been applied to wide areas.However,the open and transparent properties of the blockchains pose serious challenges to users’privacy.Among all the schemes for the privacy protection,the zero-knowledge proof algorithm conceals most of the private information in a transaction,while participants of the blockchain can validate this transaction without the private information.However,current schemes are only aimed at blockchains with the UTXO model,and only one type of assets circulates on these blockchains.Based on the zero-knowledge proof algorithm,this paper proposes a privacy protection scheme for blockchains that use the account and multi-asset model.We design the transaction structure,anonymous addresses and anonymous asset metadata,and also propose the methods of the asset transfer and double-spending detection.The zk-SNARKs algorithm is used to generate and to verify the zero-knowledge proof.And finally,we conduct the experiments to evaluate our scheme.
基金financially supported by the National Natural Science Foundation of China(No.61303216,No.61272457,No.U1401251,and No.61373172)the National High Technology Research and Development Program of China(863 Program)(No.2012AA013102)National 111 Program of China B16037 and B08038
文摘With the rapid development of computer technology, cloud-based services have become a hot topic. They not only provide users with convenience, but also bring many security issues, such as data sharing and privacy issue. In this paper, we present an access control system with privilege separation based on privacy protection(PS-ACS). In the PS-ACS scheme, we divide users into private domain(PRD) and public domain(PUD) logically. In PRD, to achieve read access permission and write access permission, we adopt the Key-Aggregate Encryption(KAE) and the Improved Attribute-based Signature(IABS) respectively. In PUD, we construct a new multi-authority ciphertext policy attribute-based encryption(CP-ABE) scheme with efficient decryption to avoid the issues of single point of failure and complicated key distribution, and design an efficient attribute revocation method for it. The analysis and simulation result show that our scheme is feasible and superior to protect users' privacy in cloud-based services.
基金This work is supported by Major Scientific and Technological Special Project of Guizhou Province(20183001)Research on the education mode for complicate skill students in new media with cross specialty integration(22150117092)+2 种基金Open Foundation of Guizhou Provincial Key Laboratory of Public Big Data(2018BDKFJJ014)Open Foundation of Guizhou Provincial Key Laboratory of Public Big Data(2018BDKFJJ019)Open Foundation of Guizhou Provincial Key Laboratory of Public Big Data(2018BDKFJJ022).
文摘In recent years,with the continuous advancement of the intelligent process of the Internet of Vehicles(IoV),the problem of privacy leakage in IoV has become increasingly prominent.The research on the privacy protection of the IoV has become the focus of the society.This paper analyzes the advantages and disadvantages of the existing location privacy protection system structure and algorithms,proposes a privacy protection system structure based on untrusted data collection server,and designs a vehicle location acquisition algorithm based on a local differential privacy and game model.The algorithm first meshes the road network space.Then,the dynamic game model is introduced into the game user location privacy protection model and the attacker location semantic inference model,thereby minimizing the possibility of exposing the regional semantic privacy of the k-location set while maximizing the availability of the service.On this basis,a statistical method is designed,which satisfies the local differential privacy of k-location sets and obtains unbiased estimation of traffic density in different regions.Finally,this paper verifies the algorithm based on the data set of mobile vehicles in Shanghai.The experimental results show that the algorithm can guarantee the user’s location privacy and location semantic privacy while satisfying the service quality requirements,and provide better privacy protection and service for the users of the IoV.
基金National Natural Science Foundation of China(Grant No.61976064)the National Natural Science Foundation of China(Grant No.62172123).
文摘The problem of data island hinders the application of big data in artificial intelligence model training,so researchers propose a federated learning framework.It enables model training without having to centralize all data in a central storage point.In the current horizontal federated learning scheme,each participant gets the final jointly trained model.No solution is proposed for scenarios where participants only provide training data in exchange for benefits,but do not care about the final jointly trained model.Therefore,this paper proposes a newboosted tree algorithm,calledRPBT(the originator Rights Protected federated Boosted Tree algorithm).Compared with the current horizontal federal learning algorithm,each participant will obtain the final jointly trained model.RPBT can guarantee that the local data of the participants will not be leaked,while the final jointly trained model cannot be obtained.It is worth mentioning that,from the perspective of the participants,the scheme uses the batch idea to make the participants participate in the training in random batches.Therefore,this scheme is more suitable for scenarios where a large number of participants are jointly modeling.Furthermore,a small number of participants will not actually participate in the joint training process.Therefore,the proposed scheme is more secure.Theoretical analysis and experimental evaluations show that RPBT is secure,accurate and efficient.
基金Introduction of Talents Lavnching Fund Project of Anhui Polytechnic University,China(No.2015YQ008)
文摘A comprehensive analysis of the impact privacy incidents on its market value is given.A broad set of instances of the exposure of personal information from a summary of some security mechanisms and the corresponding results are presented. The cumulative effect increases in magnitude over day following the breach announcement, but then decreases. Besides, a new privacy protection property, that is, p-sensitive k-anonymity is presented in this paper to protect against identity disclosure. We illustrated the inclusion of the two necessary conditions in the algorithm for computing a p-k-minimal generalization. Algorithms such as k-anonymity and l-diversity remain all sensitive attributes intact and apply generalization and suppression to the quasi-identifiers. This will keep the data "truthful" and provide good utility for data-mining applications, while achieving less perfect privacy. We aim to get the problem based on the prior analysis, and study the issue of privacy protection from the perspective of the model-benefit.