Multi-Dimensional Anonymization for Participatory Sensing Systems

Participatory sensing systems are designed to enable community people to collect, analyze, and share information for their mutual benefit in a cost-effective way. The apparently insensitive information transmitted in plaintext through the inexpensive infrastructure can be used by an eavesdrop-per to infer some sensitive information and threaten the privacy of the partic-ipating users. Participation of users cannot be ensured without assuring the privacy of the participants. Existing techniques add some uncertainty to the actual observation to achieve anonymity which, however, diminishes data quality/utility to an unacceptable extent. The subset-coding based anonymiza-tion technique, DGAS [LCN 16] provides the desired level of privacy. In this research, our objective is to overcome this limitation and design a scheme with broader applicability. We have developed a computationally efficient sub-set-coding scheme and also present a multi-dimensional anonymization tech-nique that anonymizes multiple properties of user observation, e.g. both loca-tion and product association of an observer in the context of consumer price sharing application. To the best of our knowledge, it is the first work which supports multi-dimensional anonymization in PSS. This paper also presents an in-depth analysis of adversary threats considering collusion of adversaries and different report interception patterns. Theoretical analysis, comprehensive simulation, and Android prototype based experiments are carried out to estab-lish the applicability of the proposed scheme. Also, the adversary capability is simulated to prove our scheme’s effectiveness against privacy risk.

A Framework for Enhancing Privacy and Anonymity in Blockchain-Enabled IoT Devices

With the increase in IoT(Internet of Things)devices comes an inherent challenge of security.In the world today,privacy is the prime concern of every individual.Preserving one’s privacy and keeping anonymity throughout the system is a desired functionality that does not come without inevitable trade-offs like scalability and increased complexity and is always exceedingly difficult to manage.The challenge is keeping confidentiality and continuing to make the person innominate throughout the system.To address this,we present our proposed architecture where we manage IoT devices using blockchain technology.Our proposed architecture works on and off blockchain integrated with the closed-circuit television(CCTV)security camera fixed at the rental property.In this framework,the CCTV security camera feed is redirected towards the owner and renter based on the smart contract conditions.One entity(owner or renter)can see the CCTV security camera feed at one time.There is no third-party dependence except for the CCTV security camera deployment phase.Our contributions include the proposition of framework architecture,a novel smart contract algorithm,and the modification to the ring signatures leveraging an existing cryptographic technique.Analyses are made based on different systems’security and key management areas.In an empirical study,our proposed algorithm performed better in key generation,proof generation,and verification times.By comparing similar existing schemes,we have shown the proposed architectures’advantages.Until now,we have developed this system for a specific area in the real world.However,this system is scalable and applicable to other areas like healthcare monitoring systems,which is part of our future work.

Experiments and Analyses of Anonymization Mechanisms for Trajectory Data Publishing

With the advancing of location-detection technologies and the increasing popularity of mobile phones and other location-aware devices,trajectory data is continuously growing.While large-scale trajectories provide opportunities for various applications,the locations in trajectories pose a threat to individual privacy.Recently,there has been an interesting debate on the reidentifiability of individuals in the Science magazine.The main finding of Sánchez et al.is exactly opposite to that of De Montjoye et al.,which raises the first question:"what is the true situation of the privacy preservation for trajectories in terms of reidentification?''Furthermore,it is known that anonymization typically causes a decline of data utility,and anonymization mechanisms need to consider the trade-off between privacy and utility.This raises the second question:"what is the true situation of the utility of anonymized trajectories?''To answer these two questions,we conduct a systematic experimental study,using three real-life trajectory datasets,five existing anonymization mechanisms(i.e.,identifier anonymization,grid-based anonymization,dummy trajectories,k-anonymity andε-differential privacy),and two practical applications(i.e.,travel time estimation and window range queries).Our findings reveal the true situation of the privacy preservation for trajectories in terms of reidentification and the true situation of the utility of anonymized trajectories,and essentially close the debate between De Montjoye et al.and Sánchez et al.To the best of our knowledge,this study is among the first systematic evaluation and analysis of anonymized trajectories on the individual privacy in terms of unicity and on the utility in terms of practical applications.

IDEA:A Utility-Enhanced Approach to Incomplete Data Stream Anonymization

The prevalence of missing values in the data streams collected in real environments makes them impossible to ignore in the privacy preservation of data streams.However,the development of most privacy preservation methods does not consider missing values.A few researches allow them to participate in data anonymization but introduce extra considerable information loss.To balance the utility and privacy preservation of incomplete data streams,we present a utility-enhanced approach for Incomplete Data strEam Anonymization(IDEA).In this approach,a slide-window-based processing framework is introduced to anonymize data streams continuously,in which each tuple can be output with clustering or anonymized clusters.We consider the dimensions of attribute and tuple as the similarity measurement,which enables the clustering between incomplete records and complete records and generates the cluster with minimal information loss.To avoid the missing value pollution,we propose a generalization method that is based on maybe match for generalizing incomplete data.The experiments conducted on real datasets show that the proposed approach can efficiently anonymize incomplete data streams while effectively preserving utility.

Analysing the Social Media Momo Phenomenon:Causes and Effects

The“Momo Army”is an anonymous group on social media platforms like Douban and Xiaohongshu.It uses similar avatars and nicknames to demonstrate collective identity and engage in group interactions.This group rapidly forms a strong network of interaction,establishing stable group relationships,and achieving digital invisibility.However,anonymous groups conceal anonymous violence and cyberbullying,negatively affecting individuals and society.This study will explore the reasons for the emergence of such groups,self-presented characteristics of their group members,and social impacts.It will conduct in-depth research and analysis through participant observation and interviews.

Data De-identification Framework

As technology develops,the amount of information being used has increased a lot.Every company learns big data to provide customized services with its customers.Accordingly,collecting and analyzing data of the data subject has become one of the core competencies of the companies.However,when collecting and using it,the authority of the data subject may be violated.The data often identifies its subject by itself,and even if it is not a personal information that infringes on an individual’s authority,the moment it is connected,it becomes important and sensitive personal information that we have never thought of.Therefore,recent privacy regulations such as GDPR(GeneralData ProtectionRegulation)are changing to guarantee more rights of the data subjects.To use data effectively without infringing on the rights of the data subject,the concept of de-identification has been created.Researchers and companies can make personal information less identifiable through appropriate de-identification/pseudonymization and use the data for the purpose of statistical research.De-identification/pseudonymization techniques have been studied a lot,but it is difficult for companies and researchers to know how to de-identify/pseudonymize data.It is difficult to clearly understand how and to what extent each organization should take deidentification measures.Currently,each organization does not systematically analyze and conduct the situation but only takes minimal action while looking at the guidelines distributed by each country.We solved this problem from the perspective of risk management.Several steps are required to secure the dataset starting from pre-processing to releasing the dataset.We can analyze the dataset,analyze the risk,evaluate the risk,and treat the risk appropriately.The outcomes of each step can then be used to take appropriate action on the dataset to eliminate or reduce its risk.Then,we can release the dataset under its own purpose.These series of processes were reconstructed to fit the current situation by analyzing various standards such as ISO/IEC(International Organization for Standardization/International Electrotechnical Commission)20889,NIST IR(National Institute of Standards and Technology Interagency Reports)8053,NIST SP(National Institute of Standards and Technology Special Publications)800-188,and ITU-T(International Telecommunications Union-Telecommunication)X.1148.We propose an integrated framework based on situational awareness model and risk management model.We found that this framework can be specialized for multiple domains,and it is useful because it is based on a variety of case and utility-based ROI calculations.

A New Privacy-Preserving Data Publishing Algorithm Utilizing Connectivity-Based Outlier Factor and Mondrian Techniques

Developing a privacy-preserving data publishing algorithm that stops individuals from disclosing their identities while not ignoring data utility remains an important goal to achieve.Because finding the trade-off between data privacy and data utility is an NP-hard problem and also a current research area.When existing approaches are investigated,one of the most significant difficulties discovered is the presence of outlier data in the datasets.Outlier data has a negative impact on data utility.Furthermore,k-anonymity algorithms,which are commonly used in the literature,do not provide adequate protection against outlier data.In this study,a new data anonymization algorithm is devised and tested for boosting data utility by incorporating an outlier data detection mechanism into the Mondrian algorithm.The connectivity-based outlier factor(COF)algorithm is used to detect outliers.Mondrian is selected because of its capacity to anonymize multidimensional data while meeting the needs of real-world data.COF,on the other hand,is used to discover outliers in high-dimensional datasets with complicated structures.The proposed algorithm generates more equivalence classes than the Mondrian algorithm and provides greater data utility than previous algorithms based on k-anonymization.In addition,it outperforms other algorithms in the discernibility metric(DM),normalized average equivalence class size(Cavg),global certainty penalty(GCP),query error rate,classification accuracy(CA),and F-measure metrics.Moreover,the increase in the values of theGCPand error ratemetrics demonstrates that the proposed algorithm facilitates obtaining higher data utility by grouping closer data points when compared to other algorithms.

Novel traveling quantum anonymous voting scheme via GHZ states

Based on traveling ballot mode,we propose a secure quantum anonymous voting via Greenberger–Horne–Zeilinger(GHZ)states.In this scheme,each legal voter performs unitary operation on corresponding position of particle sequence to encode his/her voting content.The voters have multiple ballot items to choose rather than just binary options“yes”or“no”.After counting votes phase,any participant who is interested in voting results can obtain the voting results.To improve the efficiency of the traveling quantum anonymous voting scheme,an optimization method based on grouping strategy is also presented.Compared with the most existing traveling quantum voting schemes,the proposed scheme is more practical because of its privacy,verifiability and non-repeatability.Furthermore,the security analysis shows that the proposed traveling quantum anonymous voting scheme can prevent various attacks and ensure high security.

An Efficient Three-Party Authenticated Key Exchange Procedure Using Chebyshev Chaotic Maps with Client Anonymity

Internet of Things(IoT)applications can be found in various industry areas,including critical infrastructure and healthcare,and IoT is one of several technological developments.As a result,tens of billions or possibly hundreds of billions of devices will be linked together.These smart devices will be able to gather data,process it,and even come to decisions on their own.Security is the most essential thing in these situations.In IoT infrastructure,authenticated key exchange systems are crucial for preserving client and data privacy and guaranteeing the security of data-in-transit(e.g.,via client identification and provision of secure communication).It is still challenging to create secure,authenticated key exchange techniques.The majority of the early authenticated key agreement procedure depended on computationally expensive and resource-intensive pairing,hashing,or modular exponentiation processes.The focus of this paper is to propose an efficient three-party authenticated key exchange procedure(AKEP)using Chebyshev chaotic maps with client anonymity that solves all the problems mentioned above.The proposed three-party AKEP is protected from several attacks.The proposed three-party AKEP can be used in practice for mobile communications and pervasive computing applications,according to statistical experiments and low processing costs.To protect client identification when transferring data over an insecure public network,our three-party AKEP may also offer client anonymity.Finally,the presented procedure offers better security features than the procedures currently available in the literature.

Identity-Based Edge Computing Anonymous Authentication Protocol

With the development of sensor technology and wireless communication technology,edge computing has a wider range of applications.The privacy protection of edge computing is of great significance.In the edge computing system,in order to ensure the credibility of the source of terminal data,mobile edge computing(MEC)needs to verify the signature of the terminal node on the data.During the signature process,the computing power of edge devices such as wireless terminals can easily become the bottleneck of system performance.Therefore,it is very necessary to improve efficiency through computational offloading.Therefore,this paper proposes an identitybased edge computing anonymous authentication protocol.The protocol realizes mutual authentication and obtains a shared key by encrypting the mutual information.The encryption algorithm is implemented through a thresholded identity-based proxy ring signature.When a large number of terminals offload computing,MEC can set the priority of offloading tasks according to the user’s identity and permissions,thereby improving offloading efficiency.Security analysis shows that the scheme can guarantee the anonymity and unforgeability of signatures.The probability of a malicious node forging a signature is equivalent to cracking the discrete logarithm puzzle.According to the efficiency analysis,in the case of MEC offloading,the computational complexity is significantly reduced,the computing power of edge devices is liberated,and the signature efficiency is improved.

RAISE:A Resilient Anonymous Information Sharing Environment

With the widespread application of cloud computing and network virtualization technologies,more and more enterprise applications are directly deployed in the cloud.However,the traditional TCP/IP network transmission model does not fully consider the information security issues caused by the uncontrollable internet environment.Network security communication solutions represented by encrypted virtual private networks(VPN)are facing multiple security threats.In fact,during the communication process,the user application needs to protect not only the content of the communication but also the behavior of the communication,such as the communication relationship,the communication protocol,and so on.Inspired by blockchain and software-defined networking technology,this paper proposes a resilient anonymous information sharing environment,RAISE.The RAISE system consists of user agents,a core switching network and a control cluster based on a consortium blockchain.User agents are responsible for segmenting,encrypting,and encapsulating user traffic.The core switching network forwards user traffic according to the rules issued by the controller,and the controller dynamically calculates the forwarding rules according to the security policy.Different from onion routing technology,RAISE adopts the controller to replace the onion routing model,which effectively overcomes the uncontrollability of nodes.The dispersed computing model is introduced to replace the TCP/IP pipeline transmission models,which overcomes the problems of anti-tracking and traffic hijacking that cannot be solved by VPNs.We propose a blockchain control plane framework,design the desired consensus algorithmand deploy a RAISE systemconsisting of 150 nodes in an internet environment.The experimental results show that the use of blockchain technology can effectively improve the reliability and security of the control plane.While maintaining high-performance network transmission,it further provides network communication security.

A Conditionally Anonymous Linkable Ring Signature for Blockchain Privacy Protection

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.

Novel Block Chain Technique for Data Privacy and Access Anonymity in Smart Healthcare

The Internet of Things (IoT) and Cloud computing are gaining popularity due to their numerous advantages, including the efficient utilization of internetand computing resources. In recent years, many more IoT applications have beenextensively used. For instance, Healthcare applications execute computations utilizing the user’s private data stored on cloud servers. However, the main obstaclesfaced by the extensive acceptance and usage of these emerging technologies aresecurity and privacy. Moreover, many healthcare data management system applications have emerged, offering solutions for distinct circumstances. But still, theexisting system has issues with specific security issues, privacy-preserving rate,information loss, etc. Hence, the overall system performance is reduced significantly. A unique blockchain-based technique is proposed to improve anonymityin terms of data access and data privacy to overcome the above-mentioned issues.Initially, the registration phase is done for the device and the user. After that, theGeo-Location and IP Address values collected during registration are convertedinto Hash values using Adler 32 hashing algorithm, and the private and publickeys are generated using the key generation centre. Then the authentication is performed through login. The user then submits a request to the blockchain server,which redirects the request to the associated IoT device in order to obtain thesensed IoT data. The detected data is anonymized in the device and stored inthe cloud server using the Linear Scaling based Rider Optimization algorithmwith integrated KL Anonymity (LSR-KLA) approach. After that, the Time-stamp-based Public and Private Key Schnorr Signature (TSPP-SS) mechanismis used to permit the authorized user to access the data, and the blockchain servertracks the entire transaction. The experimental findings showed that the proposedLSR-KLA and TSPP-SS technique provides better performance in terms of higherprivacy-preserving rate, lower information loss, execution time, and Central Processing Unit (CPU) usage than the existing techniques. Thus, the proposed method allows for better data privacy in the smart healthcare network.

The Effect of Perceived Anonymity on Online Transgressions:The Moderating Role of Moral Excuses

With the development of science and technology,the use of the Internet is becoming more and more widespread.However,with the popularity of the Internet,some problems have gradually surfaced.The anonymity of Internet use has become a breeding ground for many acts that are contrary to public decency,and this study is conducted against this background.This study explored the impact of perceived anonymity on online transgressions and investigated the moderating effect of moral excuses.A total of 414 subjects,210 males and 204 females,participated in this experimental survey.The SPSS data analysis concluded that perceived anonymity played a significant positive predictive role on online deviance(p<0.01),and the moderating role of moral excuses was not significant.This study will be conducive to the better implementation of the action of clearing cyberspace and to the regulation of public behaviour in cyberspace.

Exploring“Enabling Behaviours”of Wives of Persons with Substance Use Disorder in Chapter 8 of the Big Book of Alcoholic Anonymous

Substance use disorder has a damaging effect on the family members of alcoholics and drug users.On the other hand,the reactions and behaviours of family members may negatively influence a person with substance use disorder.The behaviours of significant others of a person with substance use disorder that contribute to the maintenance of substance use disorder are called enabling.This study aimed to explore enabling behaviours of wives of persons with substance use disorder in Chapter 8 of Alcoholic Anonymous’Big Book by utilising qualitative content analysis.Alcoholics Anonymous(AA)is one of the most commonly used programs for recovery from alcoholism.The current study sought to help mental health professionals get a better understanding of the views and premises of the AA program in reference to enabling behaviours of wives by conducting a qualitative content analysis of the AA Big Book.The study also discusses the healthy behaviours suggested by the authors of the Big Book and the comprehensiveness of the text for the readers.

网络中多敏感属性数据发布隐私保护研究

随着网络技术的快速发展,许多社会网站被创建和使用,使得关于个人的社会网络信息大量被收集和发布.为了保证个人隐私的安全,本文提出了一个新的集值属性(k,l)anonymity隐私原则,开发了一个满足这个隐私原则的隐私算法来高效地处理集值属性数据流。并通过实验进行了验证本算法的高效率和有效性。

Slicing-Based Enhanced Method for Privacy-Preserving in Publishing Big Data

Publishing big data and making it accessible to researchers is important for knowledge building as it helps in applying highly efficient methods to plan,conduct,and assess scientific research.However,publishing and processing big data poses a privacy concern related to protecting individuals’sensitive information while maintaining the usability of the published data.Several anonymization methods,such as slicing and merging,have been designed as solutions to the privacy concerns for publishing big data.However,the major drawback of merging and slicing is the random permutation procedure,which does not always guarantee complete protection against attribute or membership disclosure.Moreover,merging procedures may generatemany fake tuples,leading to a loss of data utility and subsequent erroneous knowledge extraction.This study therefore proposes a slicingbased enhanced method for privacy-preserving big data publishing while maintaining the data utility.In particular,the proposed method distributes the data into horizontal and vertical partitions.The lower and upper protection levels are then used to identify the unique and identical attributes’values.The unique and identical attributes are swapped to ensure the published big data is protected from disclosure risks.The outcome of the experiments demonstrates that the proposed method could maintain data utility and provide stronger privacy preservation.

Reproducibility and replicability of systematic reviews

Irreproducibility of research causes a major concern in academia.This concern affects all study designs regardless of scientific fields.Without testing the reproducibility and replicability it is almost impossible to repeat the research and to gain the same or similar results.In addition,irreproducibility limits the translation of research findings into practice where the same results are expected.To find the solutions,the Interacademy Partnership for Health gathered academics from established networks of science,medicine and engineering around a table to introduce seven strategies that can enhance the reproducibility:pre-registration,open methods,open data,collaboration,automation,reporting guidelines,and post-publication reviews.The current editorial discusses the generalisability and practicality of these strategies to systematic reviews and claims that systematic reviews have even a greater potential than other research designs to lead the movement toward the reproducibility of research.Moreover,I discuss the potential of reproducibility,on the other hand,to upgrade the systematic review from review to research.Furthermore,there are references to the successful and ongoing practices from collaborative efforts around the world to encourage the systematic reviewers,the journal editors and publishers,the organizations linked to evidence synthesis,and the funders and policy makers to facilitate this movement and to gain the public trust in research.

Research on Privacy Preserving Data Mining

In recent years,with the explosive development in Internet,data storage and data processing technologies,privacy preservation has been one of the greater concerns in data mining.A number of methods and techniques have been developed for privacy preserving data mining.This paper provided a wide survey of different privacy preserving data mining algorithms and analyzed the representative techniques for privacy preservation.The existing problems and directions for future research are also discussed.

Double-Blockchain Assisted Secure and Anonymous Data Aggregation for Fog-Enabled Smart Grid

As a future energy system,the smart grid is designed to improve the efficiency of traditional power systems while providing more stable and reliable services.However,this efficient and reliable service relies on collecting and analyzing users’electricity consumption data frequently,which induces various security and privacy threats.To address these challenges,we propose a double-blockchain assisted secure and anonymous data aggregation scheme for fog-enabled smart grid named DA-SADA.Specifically,we design a three-tier architecture-based data aggregation framework by integrating fog computing and the blockchain,which provides strong support for achieving efficient and secure data collection in smart grids.Subsequently,we develop a secure and anonymous data aggregation mechanism with low computational overhead by jointly leveraging the Paillier encryption,batch aggregation signature and anonymous authentication.In particular,the system achieves fine-grained data aggregation and provides effective support for power dispatching and price adjustment by the designed double-blockchain and two-level data aggregation.Finally,the superiority of the proposed scheme is illustrated by a series of security and computation cost analyses.