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.

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 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.

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.

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.

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.

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 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.

Secure motion control of micro-spacecraft using semi-homomorphic encryption

This paper studies the secure motion control problem for micro-spacecraft systems.A novel semi-homomorphic encrypted control framework,consisting of a logarithmic quantizer,two uniform quantizers,and an encrypted control law based on the Paillier cryptosystem is developed.More specifically,a logarithmic quantizer is adopted as a digitizer to convert the continuous relative motion information to digital signals.Two uniform quantizers with different quantization sensitivities are designed to encode the control gain matrix and digitized motion information to integer values.Then,we develop an encrypted state-feedback control law based on the Paillier cryptosystem,which allows the controller to compute the control input using only encrypted data.Using the Lyapunov stability theory and the homomorphic property of the Paillier cryptosystem,we prove that all signals in the closed-loop system are uniformly ultimately bounded.Different from the traditional motion control laws of spacecraft,the proposed encrypted control framework ensures the security of the exchanged data over the communication network of the spacecraft,even when communication channels are eavesdropped by malicious adversaries.Finally,we verify the effectiveness of the proposed encrypted control framework using numerical simulations.

Big Data analytics for privacy through ND-homomorphic encryption

Rapidly rising the quantity of Big Data is an opportunity to flout the privacy of people. Whenhigh processing capacity and massive storage are required for Big Data, distributed networkshave been used. There are several people involved in these activities, the system may contributeto privacy infringements frameworks have been developed for the preservation of privacy atvarious levels (e.g. information age, information the executives and information preparing) asfor the existing pattern of huge information. We plan to frame this paper as a literature surveyof these classifications, including the Privacy Processes in Big Data and the presentation of theAssociate Challenges. Homomorphic encryption is particularised aimed at solitary single actionon the ciphered information. Homomorphic enciphering is restrained to an honest operation onthe encoded data. The reference to encryption project fulfils many accurate trading operationson coded numerical data;therefore, it protects the written in code-sensible information evenmore.

Secure storage and accessing the data in cloud using optimized homomorphic encryption

Different efforts have been undertaken to customizing a security and privacy concern in clouddata access. Therefore, the security measures are reliable and the data access was verified as themajor problem in the cloud environment. To overcome this problem, we proposed an efficientdata access control using optimized homomorphic encryption (HE). Because users outsourcetheir sensitive information to cloud providers, data security and access control is one of themost difficult ongoing cloud computing research projects. Existing solutions that rely on cryptographictechnologies to address these security issues result in significant complexity for bothdata and cloud service providers. The experimental results show that the key generation is 7.6%decreased by HE and 14.14% less than the proposed method. The encryption time is 11.34% lessthan the optimized HE and 23.28% decreased by ECC. The decryption time is 13.18% and 24.07%when compared with HE and ECC respectively.

A Blockchain-Based Proxy Re-Encryption Scheme with Conditional Privacy Protection and Auditability

With the development of Internet of Things technology,intelligent door lock devices are widely used in the field of house leasing.In the traditional housing leasing scenario,problems of door lock information disclosure,tenant privacy disclosure and rental contract disputes frequently occur,and the security,fairness and auditability of the housing leasing transaction cannot be guaranteed.To solve the above problems,a blockchain-based proxy re-encryption scheme with conditional privacy protection and auditability is proposed.The scheme implements fine-grained access control of door lock data based on attribute encryption technology with policy hiding,and uses proxy re-encryption technology to achieve auditable supervision of door lock information transactions.Homomorphic encryption technology and zero-knowledge proof technology are introduced to ensure the confidentiality of housing rent information and the fairness of rent payment.To construct a decentralized housing lease transaction architecture,the scheme realizes the efficient collaboration between the door lock data ciphertext stored under the chain and the key information ciphertext on the chain based on the blockchain and InterPlanetary File System.Finally,the security proof and computing performance analysis of the proposed scheme are carried out.The results show that the scheme can resist the chosen plaintext attack and has low computational cost.

Secure Scheme for Locating Disease-Causing Genes Based on Multi-Key Homomorphic Encryption

Genes have great significance for the prevention and treatment of some diseases.A vital consideration is the need to find a way to locate pathogenic genes by analyzing the genetic data obtained from different medical institutions while protecting the privacy of patients’genetic data.In this paper,we present a secure scheme for locating disease-causing genes based on Multi-Key Homomorphic Encryption(MKHE),which reduces the risk of leaking genetic data.First,we combine MKHE with a frequency-based pathogenic gene location function.The medical institutions use MKHE to encrypt their genetic data.The cloud then homomorphically evaluates specific gene-locating circuits on the encrypted genetic data.Second,whereas most location circuits are designed only for locating monogenic diseases,we propose two location circuits(TH-intersection and Top-q)that can locate the disease-causing genes of polygenic diseases.Third,we construct a directed decryption protocol in which the users involved in the homomorphic evaluation can appoint a target user who can obtain the final decryption result.Our experimental results show that compared to the JWB+17 scheme published in the journal Science,our scheme can be used to diagnose polygenic diseases,and the participants only need to upload their encrypted genetic data once,which reduces the communication traffic by a few hundred-fold.

Targeted Fully Homomorphic Encryption Based on a Double Decryption Algorithm for Polynomials

Several public-key encryption schemes used to solve the problem of ciphertext data processing on the fly are discussed. A new targeted fully homomorphic encryption scheme based on the discrete logarithm problem is presented. Public-key encryption cryptosystems are classified to examine homomorphic encryption. Without employing techniques proposed by Gentry such as somewhat homomorphic and bootstrapping techniques, or relinearization technique proposed by Brakerski et al., a new method called ＂Double Decryption Algorithm＂ is employed in our cryptography to satisfy a fully or targeted fully homomorphic property. Inspired by EIGamal and BGN cryptography, we obtain the desired fully homomorphic property by selecting a new group and adding an extra component to the ciphertext. Proof of semantic security is also demonstrated.

Modified Multi-Key Fully Homomorphic Encryption Based on NTRU Cryptosystem without Key-Switching

The Multi-Key Fully Homomorphic Encryption (MKFHE) based on the NTRU cryptosystem is an important alternative to the post-quantum cryptography due to its simple scheme form,high efficiency,and fewer ciphertexts and keys.In 2012,Lopez-Alt et al.proposed the first NTRU-type MKFHE scheme,the LTV12 scheme,using the key-switching and modulus-reduction techniques,whose security relies on two assumptions:the Ring Learning With Error (RLWE) assumption and the Decisional Small Polynomial Ratio (DSPR) assumption.However,the LTV12and subsequent NTRU-type schemes are restricted to the family of power-of-2 cyclotomic rings,which may affect the security in the case of subfield attacks.Moreover,the key-switching technique of the LTV12 scheme requires a circular application of evaluation keys,which causes rapid growth of the error and thus affects the circuit depth.In this paper,an NTRU-type MKFHE scheme over prime cyclotomic rings without key-switching is proposed,which has the potential to resist the subfield attack and decrease the error exponentially during the homomorphic evaluating process.First,based on the RLWE and DSPR assumptions over the prime cyclotomic rings,a detailed analysis of the factors affecting the error during the homomorphic evaluations in the LTV12 scheme is provided.Next,a Low Bit Discarded&Dimension Expansion of Ciphertexts (LBD&DEC) technique is proposed,and the inherent homomorphic multiplication decryption structure of the NTRU is proposed,which can eliminate the key-switching operation in the LTV12 scheme.Finally,a leveled NTRU-type MKFHE scheme is developed using the LBD&DEC and modulus-reduction techniques.The analysis shows that the proposed scheme compared to the LTV12 scheme can decrease the magnitude of the error exponentially and minimize the dimension of ciphertexts.

Homomorphic encryption experiments on IBM＇s cloud quantum computing platform

Quantum computing has undergone rapid development in recent years. Owing to limitations on scalability, personal quantum computers still seem slightly unrealistic in the near future. The first practical quantum computer for ordinary users is likely to be on the cloud. However, the adoption of cloud computing is possible only if security is ensured. Homomorphic encryption is a cryptographic protocol that allows computation to be performed on encrypted data without decrypting them, so it is well suited to cloud computing. Here, we first applied homomorphic encryption on IBM＇s cloud quantum computer platform. In our experiments, we successfully implemented a quantum algorithm for linear equations while protecting our privacy. This demonstration opens a feasible path to the next stage of development of cloud quantum information technology.