Distributed Privacy-Preserving Fusion Estimation Using Homomorphic Encryption

The privacy-preserving problem for distributed fusion estimation scheme is concerned in this paper.When legitimate user wants to obtain consistent information from multiple sensors,it always employs a fusion center(FC)to gather local data and compute distributed fusion estimates(DFEs).Due to the existence of potential eavesdropper,the data exchanged among sensors,FC and user imperatively require privacy preservation.Hence,we propose a distributed confidentiality fusion structure against eavesdropper by using Paillier homomorphic encryption approach.In this case,FC cannot acquire real values of local state estimates,while it only helps calculate encrypted DFEs.Then,the legitimate user can successfully obtain the true values of DFEs according to the encrypted information and secret keys,which is based on the homomorphism of encryption.Finally,an illustrative example is provided to verify the effectiveness of the proposed methods.

Enhancing IoT Data Security with Lightweight Blockchain and Okamoto Uchiyama Homomorphic Encryption

Blockchain technology has garnered significant attention from global organizations and researchers due to its potential as a solution for centralized system challenges.Concurrently,the Internet of Things(IoT)has revolutionized the Fourth Industrial Revolution by enabling interconnected devices to offer innovative services,ultimately enhancing human lives.This paper presents a new approach utilizing lightweight blockchain technology,effectively reducing the computational burden typically associated with conventional blockchain systems.By integrating this lightweight blockchain with IoT systems,substantial reductions in implementation time and computational complexity can be achieved.Moreover,the paper proposes the utilization of the Okamoto Uchiyama encryption algorithm,renowned for its homomorphic characteristics,to reinforce the privacy and security of IoT-generated data.The integration of homomorphic encryption and blockchain technology establishes a secure and decentralized platformfor storing and analyzing sensitive data of the supply chain data.This platformfacilitates the development of some business models and empowers decentralized applications to perform computations on encrypted data while maintaining data privacy.The results validate the robust security of the proposed system,comparable to standard blockchain implementations,leveraging the distinctive homomorphic attributes of the Okamoto Uchiyama algorithm and the lightweight blockchain paradigm.

FL-EASGD:Federated Learning Privacy Security Method Based on Homomorphic Encryption

Federated learning ensures data privacy and security by sharing models among multiple computing nodes instead of plaintext data.However,there is still a potential risk of privacy leakage,for example,attackers can obtain the original data through model inference attacks.Therefore,safeguarding the privacy of model parameters becomes crucial.One proposed solution involves incorporating homomorphic encryption algorithms into the federated learning process.However,the existing federated learning privacy protection scheme based on homomorphic encryption will greatly reduce the efficiency and robustness when there are performance differences between parties or abnormal nodes.To solve the above problems,this paper proposes a privacy protection scheme named Federated Learning-Elastic Averaging Stochastic Gradient Descent(FL-EASGD)based on a fully homomorphic encryption algorithm.First,this paper introduces the homomorphic encryption algorithm into the FL-EASGD scheme to preventmodel plaintext leakage and realize privacy security in the process ofmodel aggregation.Second,this paper designs a robust model aggregation algorithm by adding time variables and constraint coefficients,which ensures the accuracy of model prediction while solving performance differences such as computation speed and node anomalies such as downtime of each participant.In addition,the scheme in this paper preserves the independent exploration of the local model by the nodes of each party,making the model more applicable to the local data distribution.Finally,experimental analysis shows that when there are abnormalities in the participants,the efficiency and accuracy of the whole protocol are not significantly affected.

Privacy-Preserving Multi-Keyword Fuzzy Adjacency Search Strategy for Encrypted Graph in Cloud Environment

In a cloud environment,outsourced graph data is widely used in companies,enterprises,medical institutions,and so on.Data owners and users can save costs and improve efficiency by storing large amounts of graph data on cloud servers.Servers on cloud platforms usually have some subjective or objective attacks,which make the outsourced graph data in an insecure state.The issue of privacy data protection has become an important obstacle to data sharing and usage.How to query outsourcing graph data safely and effectively has become the focus of research.Adjacency query is a basic and frequently used operation in graph,and it will effectively promote the query range and query ability if multi-keyword fuzzy search can be supported at the same time.This work proposes to protect the privacy information of outsourcing graph data by encryption,mainly studies the problem of multi-keyword fuzzy adjacency query,and puts forward a solution.In our scheme,we use the Bloom filter and encryption mechanism to build a secure index and query token,and adjacency queries are implemented through indexes and query tokens on the cloud server.Our proposed scheme is proved by formal analysis,and the performance and effectiveness of the scheme are illustrated by experimental analysis.The research results of this work will provide solid theoretical and technical support for the further popularization and application of encrypted graph data processing technology.

Multi-Source Data Privacy Protection Method Based on Homomorphic Encryption and Blockchain

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.

Novel Homomorphic Encryption for Mitigating Impersonation Attack in Fog Computing

Fog computing is a rapidly growing technology that aids in pipelining the possibility of mitigating breaches between the cloud and edge servers.It facil-itates the benefits of the network edge with the maximized probability of offering interaction with the cloud.However,the fog computing characteristics are suscep-tible to counteract the challenges of security.The issues present with the Physical Layer Security(PLS)aspect in fog computing which included authentication,integrity,and confidentiality has been considered as a reason for the potential issues leading to the security breaches.In this work,the Octonion Algebra-inspired Non-Commutative Ring-based Fully Homomorphic Encryption Scheme(NCR-FHE)was proposed as a secrecy improvement technique to overcome the impersonation attack in cloud computing.The proposed approach was derived through the benefits of Octonion algebra to facilitate the maximum security for big data-based applications.The major issues in the physical layer security which may potentially lead to the possible security issues were identified.The potential issues causing the impersonation attack in the Fog computing environment were identified.The proposed approach was compared with the existing encryption approaches and claimed as a robust approach to identify the impersonation attack for the fog and edge network.The computation cost of the proposed NCR-FHE is identified to be significantly reduced by 7.18%,8.64%,9.42%,and 10.36%in terms of communication overhead for varying packet sizes,when compared to the benchmarked ECDH-DH,LHPPS,BF-PHE and SHE-PABF schemes.

An Unbounded Fully Homomorphic Encryption Scheme Based on Ideal Lattices and Chinese Remainder Theorem

We propose an unbounded fully homomorphic encryption scheme, i.e. a scheme that allows one to compute on encrypted data for any desired functions without needing to decrypt the data or knowing the decryption keys. This is a rational solution to an old problem proposed by Rivest, Adleman, and Dertouzos [1] in 1978, and to some new problems that appeared in Peikert [2] as open questions 10 and open questions 11 a few years ago. Our scheme is completely different from the breakthrough work [3] of Gentry in 2009. Gentry’s bootstrapping technique constructs a fully homomorphic encryption (FHE) scheme from a somewhat homomorphic one that is powerful enough to evaluate its own decryption function. To date, it remains the only known way of obtaining unbounded FHE. Our construction of an unbounded FHE scheme is straightforward and can handle unbounded homomorphic computation on any refreshed ciphertexts without bootstrapping transformation technique.

A blockchain based privacy-preserving federated learning scheme for Internet of Vehicles

The application of artificial intelligence technology in Internet of Vehicles(lov)has attracted great research interests with the goal of enabling smart transportation and traffic management.Meanwhile,concerns have been raised over the security and privacy of the tons of traffic and vehicle data.In this regard,Federated Learning(FL)with privacy protection features is considered a highly promising solution.However,in the FL process,the server side may take advantage of its dominant role in model aggregation to steal sensitive information of users,while the client side may also upload malicious data to compromise the training of the global model.Most existing privacy-preserving FL schemes in IoV fail to deal with threats from both of these two sides at the same time.In this paper,we propose a Blockchain based Privacy-preserving Federated Learning scheme named BPFL,which uses blockchain as the underlying distributed framework of FL.We improve the Multi-Krum technology and combine it with the homomorphic encryption to achieve ciphertext-level model aggregation and model filtering,which can enable the verifiability of the local models while achieving privacy-preservation.Additionally,we develop a reputation-based incentive mechanism to encourage users in IoV to actively participate in the federated learning and to practice honesty.The security analysis and performance evaluations are conducted to show that the proposed scheme can meet the security requirements and improve the performance of the FL model.

PEPFL:A framework for a practical and efficient privacy-preserving federated learning

As an emerging joint learning model,federated learning is a promising way to combine model parameters of different users for training and inference without collecting users’original data.However,a practical and efficient solution has not been established in previous work due to the absence of efficient matrix computation and cryptography schemes in the privacy-preserving federated learning model,especially in partially homomorphic cryptosystems.In this paper,we propose a Practical and Efficient Privacy-preserving Federated Learning(PEPFL)framework.First,we present a lifted distributed ElGamal cryptosystem for federated learning,which can solve the multi-key problem in federated learning.Secondly,we develop a Practical Partially Single Instruction Multiple Data(PSIMD)parallelism scheme that can encode a plaintext matrix into single plaintext for encryption,improving the encryption efficiency and reducing the communication cost in partially homomorphic cryptosystem.In addition,based on the Convolutional Neural Network(CNN)and the designed cryptosystem,a novel privacy-preserving federated learning framework is designed by using Momentum Gradient Descent(MGD).Finally,we evaluate the security and performance of PEPFL.The experiment results demonstrate that the scheme is practicable,effective,and secure with low communication and computation costs.

Privacy-Preserving Federated Deep Learning Diagnostic Method for Multi-Stage Diseases

Diagnosing multi-stage diseases typically requires doctors to consider multiple data sources,including clinical symptoms,physical signs,biochemical test results,imaging findings,pathological examination data,and even genetic data.When applying machine learning modeling to predict and diagnose multi-stage diseases,several challenges need to be addressed.Firstly,the model needs to handle multimodal data,as the data used by doctors for diagnosis includes image data,natural language data,and structured data.Secondly,privacy of patients’data needs to be protected,as these data contain the most sensitive and private information.Lastly,considering the practicality of the model,the computational requirements should not be too high.To address these challenges,this paper proposes a privacy-preserving federated deep learning diagnostic method for multi-stage diseases.This method improves the forward and backward propagation processes of deep neural network modeling algorithms and introduces a homomorphic encryption step to design a federated modeling algorithm without the need for an arbiter.It also utilizes dedicated integrated circuits to implement the hardware Paillier algorithm,providing accelerated support for homomorphic encryption in modeling.Finally,this paper designs and conducts experiments to evaluate the proposed solution.The experimental results show that in privacy-preserving federated deep learning diagnostic modeling,the method in this paper achieves the same modeling performance as ordinary modeling without privacy protection,and has higher modeling speed compared to similar algorithms.

A Method of Homomorphic Encryption

The existing homomorphie eneryption scheme is based on ring of the integer, and the possible operators are restricted to addition and multiplication only. In this paper, a new operation is defined Similar Modul. Base on the Similar Modul, the number sets of the homomorphic encryption scheme is extended to real number, and the possible operators are extended to addition, subtraction, multiplication and division. Our new approach provides a practical ways of implementation because of the extension of the operators and the number sets.

A Fully Homomorphic Encryption Scheme with Better Key Size

Fully homomorphic encryption is faced with two problems now. One is candidate fully homomorphic encryption schemes are few. Another is that the efficiency of fully homomorphic encryption is a big question. In this paper, we propose a fully homomorphic encryption scheme based on LWE, which has better key size. Our main contributions are： （1） According to the binary-LWE recently, we choose secret key from binary set and modify the basic encryption scheme proposed in Linder and Peikert in 2010. We propose a fully homomorphic encryption scheme based on the new basic encryption scheme. We analyze the correctness and give the proof of the security of our scheme. The public key, evaluation keys and tensored ciphertext have better size in our scheme. （2） Estimating parameters for fully homomorphic encryption scheme is an important work. We estimate the concert parameters for our scheme. We compare these parameters between our scheme and Bral2 scheme. Our scheme have public key and private key that smaller by a factor of about logq than in Bral2 scheme. Tensored ciphertext in our scheme is smaller by a factor of about log2q than in Bral2 scheme. Key switching matrix in our scheme is smaller by a factor of about log3q than in Bra12 scheme.

Secure and privacy-preserving DRM scheme using homomorphic encryption in cloud computing

Cloud computing provides a convenient way of content trading and sharing. In this paper, we propose a secure and privacy-preserving digital rights management （DRM） scheme using homomorphic encryption in cloud computing. We present an efficient digital rights management framework in cloud computing, which allows content provider to outsource encrypted contents to centralized content server and allows user to consume contents with the license issued by license server. Further, we provide a secure content key distribution scheme based on additive homomorphic probabilistic public key encryption and proxy re-encryption. The provided scheme prevents malicious employees of license server from issuing the license to unauthorized user. In addition, we achieve privacy preserving by allowing users to stay anonymous towards the key server and service provider. The analysis and comparison results indicate that the proposed scheme has high efficiency and security.

A secure outsourced Turing- equivalent computation scheme against semi-honest workers using fully homomorphic encryption

A scheme that can realize homomorphic Turing- equivalent privacy-preserving computations is proposed, where the encoding of the Turing machine is independent of its inputs and running time. Several extended private information retrieval protocols based on fully homomorphic encryption are designed, so that the reading and writing of the tape of the Turing machine, as well as the evaluation of the transition function of the Turing machine, can be performed by the permitted Boolean circuits of fully homomorphic encryption schemes. This scheme overwhelms the Turing-machine-to- circuit conversion approach, which also implements the Turing-equivalent computation. The encoding of a Turing- machine-to-circuit conversion approach is dependent on both the input data and the worst-case runtime. The proposed scheme efficiently provides the confidentiality of both program and data of the delegator in the delegator-worker model of outsourced computation against semi-honest workers.

Universal quantum circuit evaluation on encrypted data using probabilistic quantum homomorphic encryption scheme

Homomorphic encryption has giant advantages in the protection of privacy information.In this paper,we present a new kind of probabilistic quantum homomorphic encryption scheme for the universal quantum circuit evaluation.Firstly,the pre-shared non-maximally entangled states are utilized as auxiliary resources,which lower the requirements of the quantum channel,to correct the errors in non-Clifford gate evaluation.By using the set synthesized by Clifford gates and T gates,it is feasible to perform the arbitrary quantum computation on the encrypted data.Secondly,our scheme is different from the previous scheme described by the quantum homomorphic encryption algorithm.From the perspective of application,a two-party probabilistic quantum homomorphic encryption scheme is proposed.It is clear what the computation and operation that the client and the server need to perform respectively,as well as the permission to access the data.Finally,the security of probabilistic quantum homomorphic encryption scheme is analyzed in detail.It demonstrates that the scheme has favorable security in three aspects,including privacy data,evaluated data and encryption and decryption keys.

A Privacy Preserving Deep Linear Regression Scheme Based on Homomorphic Encryption

This paper proposes a strategy for machine learning in the ciphertext domain.The data to be trained in the linear regression equation is encrypted by SHE homomorphic encryption,and then trained in the ciphertext domain.At the same time,it is guaranteed that the error of the training results between the ciphertext domain and the plaintext domain is in a controllable range.After the training,the ciphertext can be decrypted and restored to the original plaintext training data.

Efficient Security Sequencing Problem over Insecure Channel Based on Homomorphic Encryption

In the field of sequencing of secret number,an important problem is how to establish an efficient and secure protocol for sorting the secret number.As a powerful tool in solving privacy sequencing problems,secure multipart computation is more and more popular in anonymous voting and online auction.In the present study,related secure computation protocol for sequencing problem is not many by far.In order to improve the efficiency and safety,we propose a security sequencing protocol based on homomorphic encryption.We also give analysis of correctness and security to highlight its feasibility.

Road Distance Computation Using Homomorphic Encryption in Road Networks

Road networks have been used in a wide range of applications to reduces the cost of transportation and improve the quality of related services.The shortest road distance computation has been considered as one of the most fundamental operations of road networks computation.To alleviate privacy concerns about location privacy leaks during road distance computation,it is desirable to have a secure and efficient road distance computation approach.In this paper,we propose two secure road distance computation approaches,which can compute road distance over encrypted data efficiently.An approximate road distance computation approach is designed by using Partially Homomorphic Encryption and road network set embedding.An exact road distance computation is built by using Somewhat Homomorphic Encryption and road network hypercube embedding.We implement our two road distance computation approaches,and evaluate them on the real cityscale road network.Evaluation results show that our approaches are accurate and efficient.

A Secure Multiparty Quantum Homomorphic Encryption Scheme

The significant advantage of the quantum homomorphic encryption scheme is to ensure the perfect security of quantum private data.In this paper,a novel secure multiparty quantum homomorphic encryption scheme is proposed,which can complete arbitrary quantum computation on the private data of multiple clients without decryption by an almost dishonest server.Firstly,each client obtains a secure encryption key through the measurement device independent quantum key distribution protocol and encrypts the private data by using the encryption operator and key.Secondly,with the help of the almost dishonest server,the non-maximally entangled states are preshared between the client and the server to correct errors in the homomorphic evaluation of T gates,so as to realize universal quantum circuit evaluation on encrypted data.Thirdly,from the perspective of the application scenario of secure multi-party computation,this work is based on the probabilistic quantum homomorphic encryption scheme,allowing multiple parties to delegate the server to perform the secure homomorphic evaluation.The operation and the permission to access the data performed by the client and the server are clearly pointed out.Finally,a concrete security analysis shows that the proposed multiparty quantum homomorphic encryption scheme can securely resist outside and inside attacks.

A Certificateless Homomorphic Encryption Scheme for Protecting Transaction Data Privacy of Post-Quantum Blockchain

Blockchain has a profound impact on all areas of society by virtue of its immutability,decentralization and other characteristics.However,blockchain faces the problem of data privacy leakage during the application process,and the rapid development of quantum computing also brings the threat of quantum attack to blockchain.In this paper,we propose a lattice-based certificateless fully homomorphic encryption(LCFHE)algorithm based on approximate eigenvector firstly.And we use the lattice-based delegate algorithm and preimage sampling algorithm to extract part of the private key based on certificateless scheme,which is composed of the private key together with the secret value selected by the user,thus effectively avoiding the problems of certificate management and key escrow.Secondly,we propose a post-quantum blockchain transaction privacy protection scheme based on LCFHE algorithm,which uses the ciphertext calculation characteristic of homomorphic encryption to encrypt the account balance and transaction amount,effectively protecting the transaction privacy of users and having the ability to resist quantum attacks.Finally,we analyze the correctness and security of LCFHE algorithm,and the security of the algorithm reduces to the hardness of learning with errors(LWE)hypothesis.