As data analysis often incurs significant communication and computational costs,these tasks are increasingly outsourced to cloud computing platforms.However,this introduces privacy concerns,as sensitive data must be t...As data analysis often incurs significant communication and computational costs,these tasks are increasingly outsourced to cloud computing platforms.However,this introduces privacy concerns,as sensitive data must be transmitted to and processed by untrusted parties.To address this,fully homomorphic encryption(FHE)has emerged as a promising solution for privacy-preserving Machine-Learning-as-a-Service(MLaaS),enabling computation on encrypted data without revealing the plaintext.Nevertheless,FHE remains computationally expensive.As a result,approximate homomorphic encryption(AHE)schemes,such as CKKS,have attracted attention due to their efficiency.In our previous work,we proposed RP-OKC,a CKKS-based clustering scheme implemented via TenSEAL.However,errors inherent to CKKS operations—termed CKKS-errors—can affect the accuracy of the result after decryption.Since these errors can be mitigated through post-decryption rounding,we propose a data pre-scaling technique to increase the number of significant digits and reduce CKKS-errors.Furthermore,we introduce an Operation-Error-Estimation(OEE)table that quantifies upper-bound error estimates for various CKKS operations.This table enables error-aware decryption correction,ensuring alignment between encrypted and plaintext results.We validate our method on K-means clustering using the Kaggle Customer Segmentation dataset.Experimental results confirm that the proposed scheme enhances the accuracy and reliability of privacy-preserving data analysis in cloud environments.展开更多
Due to the rapid advancement of information technology,data has emerged as the core resource driving decision-making and innovation across all industries.As the foundation of artificial intelligence,machine learning(M...Due to the rapid advancement of information technology,data has emerged as the core resource driving decision-making and innovation across all industries.As the foundation of artificial intelligence,machine learning(ML)has expanded its applications into intelligent recommendation systems,autonomous driving,medical diagnosis,and financial risk assessment.However,it relies on massive datasets,which contain sensitive personal information.Consequently,Privacy-Preserving Machine Learning(PPML)has become a critical research direction.To address the challenges of efficiency and accuracy in encrypted data computation within PPML,Homomorphic Encryption(HE)technology is a crucial solution,owing to its capability to facilitate computations on encrypted data.However,the integration of machine learning and homomorphic encryption technologies faces multiple challenges.Against this backdrop,this paper reviews homomorphic encryption technologies,with a focus on the advantages of the Cheon-Kim-Kim-Song(CKKS)algorithm in supporting approximate floating-point computations.This paper reviews the development of three machine learning techniques:K-nearest neighbors(KNN),K-means clustering,and face recognition-in integration with homomorphic encryption.It proposes feasible schemes for typical scenarios,summarizes limitations and future optimization directions.Additionally,it presents a systematic exploration of the integration of homomorphic encryption and machine learning from the essence of the technology,application implementation,performance trade-offs,technological convergence and future pathways to advance technological development.展开更多
Ciphertext data retrieval in cloud databases suffers from some critical limitations,such as inadequate security measures,disorganized key management practices,and insufficient retrieval access control capabilities.To ...Ciphertext data retrieval in cloud databases suffers from some critical limitations,such as inadequate security measures,disorganized key management practices,and insufficient retrieval access control capabilities.To address these problems,this paper proposes an enhanced Fully Homomorphic Encryption(FHE)algorithm based on an improved DGHV algorithm,coupled with an optimized ciphertext retrieval scheme.Our specific contributions are outlined as follows:First,we employ an authorization code to verify the user’s retrieval authority and perform hierarchical access control on cloud storage data.Second,a triple-key encryption mechanism,which separates the data encryption key,retrieval authorization key,and retrieval key,is designed.Different keys are provided to different entities to run corresponding system functions.The key separation architecture proves particularly advantageous in multi-verifier coexistence scenarios,environments involving untrusted third-party retrieval services.Finally,the enhanced DGHV-based retrieval mechanism extends conventional functionality by enabling multi-keyword queries with similarity-ranked results,thereby significantly improving both the functionality and usability of the FHE system.展开更多
With increasing demand for data circulation,ensuring data security and privacy is paramount,specifically protecting privacy while maximizing utility.Blockchain,while decentralized and transparent,faces challenges in p...With increasing demand for data circulation,ensuring data security and privacy is paramount,specifically protecting privacy while maximizing utility.Blockchain,while decentralized and transparent,faces challenges in privacy protection and data verification,especially for sensitive data.Existing schemes often suffer from inefficiency and high overhead.We propose a privacy protection scheme using BGV homomorphic encryption and Pedersen Secret Sharing.This scheme enables secure computation on encrypted data,with Pedersen sharding and verifying the private key,ensuring data consistency and immutability.The blockchain framework manages key shards,verifies secrets,and aids security auditing.This approach allows for trusted computation without revealing the underlying data.Preliminary results demonstrate the scheme's feasibility in ensuring data privacy and security,making data available but not visible.This study provides an effective solution for data sharing and privacy protection in blockchain applications.展开更多
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 ...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.展开更多
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 obta...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.展开更多
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 revol...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.展开更多
Digital twin is a novel technology that has achieved significant progress in industrial manufactur-ing systems in recent years.In the digital twin envi-ronment,entities in the virtual space collect data from devices i...Digital twin is a novel technology that has achieved significant progress in industrial manufactur-ing systems in recent years.In the digital twin envi-ronment,entities in the virtual space collect data from devices in the physical space to analyze their states.However,since a lot of devices exist in the physical space,the digital twin system needs to aggregate data from multiple devices at the edge gateway.Homomor-phic integrity and confidentiality protections are two important requirements for this data aggregation pro-cess.Unfortunately,existing homomorphic encryp-tion algorithms do not support integrity protection,and existing homomorphic signing algorithms require all signers to use the same signing key,which is not feasible in the digital twin environment.Moreover,for both integrity and confidentiality protections,the homomorphic signing algorithm must be compatible with the aggregation manner of the homomorphic en-cryption algorithm.To address these issues,this paper designs a novel homomorphic aggregation scheme,which allows multiple devices in the physical space to sign different data using different keys and support in-tegrity and confidentiality protections.Finally,the security of the newly designed scheme is analyzed,and its efficiency is evaluated.Experimental results show that our scheme is feasible for real world applications.展开更多
False Data Injection Attack(FDIA),a disruptive cyber threat,is becoming increasingly detrimental to smart grids with the deepening integration of information technology and physical power systems,leading to system unr...False Data Injection Attack(FDIA),a disruptive cyber threat,is becoming increasingly detrimental to smart grids with the deepening integration of information technology and physical power systems,leading to system unreliability,data integrity loss and operational vulnerability exposure.Given its widespread harm and impact,conducting in-depth research on FDIA detection is vitally important.This paper innovatively introduces a FDIA detection scheme:A Protected Federated Deep Learning(ProFed),which leverages Federated Averaging algorithm(FedAvg)as a foundational framework to fortify data security,harnesses pre-trained enhanced spatial-temporal graph neural networks(STGNN)to perform localized model training and integrates the Cheon-Kim-Kim-Song(CKKS)homomorphic encryption system to secure sensitive information.Simulation tests on IEEE 14-bus and IEEE 118-bus systems demonstrate that our proposed method outperforms other state-of-the-art detection methods across all evaluation metrics,with peak improvements reaching up to 35%.展开更多
In this study,we investigated privacy-preserving ID3 Decision Tree(PPID3)training and inference based on fully homomorphic encryption(FHE),which has not been actively explored due to the high computational cost associ...In this study,we investigated privacy-preserving ID3 Decision Tree(PPID3)training and inference based on fully homomorphic encryption(FHE),which has not been actively explored due to the high computational cost associated with managing numerous child nodes in an ID3 tree.We propose HEaaN-ID3,a novel approach to realize PPID3 using the Cheon-Kim-Kim-Song(CKKS)scheme.HEaaN-ID3 is the first FHE-based ID3 framework that completes both training and inference without any intermediate decryption,which is especially valuable when decryption keys are inaccessible or a single-cloud security domain is assumed.To enhance computational efficiency,we adopt a modified Gini impurity(MGI)score instead of entropy to evaluate information gain,thereby avoiding costly inverse operations.In addition,we fully leverage the Single Instruction Multiple Data(SIMD)property of CKKS to parallelize computations at multiple tree nodes.Unlike previous approaches that require decryption at each node or rely on two-party secure computation,our method enables a fully non-interactive training and inference pipeline in the encrypted domain.We validated the proposed scheme using UCI datasets with both numerical and nominal features,demonstrating inference accuracy comparable to plaintext implementations in Scikit-Learn.Moreover,experiments show that HEaaN-ID3 significantly reduces training and inference time per node relative to earlier FHE-based approaches.展开更多
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 ...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.展开更多
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 pap...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.展开更多
Although the learning with errors（LWE）-based full homomorphic encryption scheme was the first example of deviation from the original Gentry＇s blueprint, the scheme did not give detailed conversion process of circui...Although the learning with errors（LWE）-based full homomorphic encryption scheme was the first example of deviation from the original Gentry＇s blueprint, the scheme did not give detailed conversion process of circuit layer structure, and must rely on bootstrapping technique to achieve full homomorphism. Therefore, through modifying the re-linearization technique proposed by the above scheme, a technique called non-matrix key switching is presented, which includes key switching with re-linearization and pure key switching. The complex matrix operations of existing key switching technique are removed. Combining this technique with modulus switching, a （leveled） fully homomorphic encryption scheme without bootstrapping from LWE is constructed. In order to make circuit layer structure clear, the scheme gives detailed refresh door operation. Finally, we use bootstrapping to upgrade arithmetic circuit to any layer, and make the homomorphic computing capability of the scheme have nothing to circuit depth.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
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 in...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.展开更多
In order to guarantee the user's privacy and the integrity of data when retrieving ciphertext in an untrusted cloud environment, an improved ciphertext retrieval scheme was proposed based on full homomorphic encry...In order to guarantee the user's privacy and the integrity of data when retrieving ciphertext in an untrusted cloud environment, an improved ciphertext retrieval scheme was proposed based on full homomorphic encryption. This scheme can encrypt two bits one time and improve the efficiency of retrieval. Moreover, it has small key space and reduces the storage space. Meanwhile, the homomorphic property of this scheme was proved in detail. The experimental results and comparisons show that the proposed scheme is characterized by increased security, high efficiency and low cost.展开更多
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 evaluat...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.展开更多
With the rapid development of information network,the computing resources and storage capacity of ordinary users cannot meet their needs of data processing.The emergence of cloud computing solves this problem but brin...With the rapid development of information network,the computing resources and storage capacity of ordinary users cannot meet their needs of data processing.The emergence of cloud computing solves this problem but brings data security problems.How to manage and retrieve ciphertext data effectively becomes a challenging problem.To these problems,a new image retrieval method in ciphertext domain by block image encrypting based on Paillier homomophic cryptosystem is proposed in this paper.This can be described as follows:According to the Paillier encryption technology,the image owner encrypts the original image in blocks,obtains the image in ciphertext domain,then passes it to the third party server.The server calculates the difference histogram of the image in ciphertext domain according to the public key and establishes the index database.The user passes the retrieved image to the server.The server computes the differential histogram of the retrieved image by public key.Then,compares the similarity of it with the histogram in index database and selects larger similarity images in ciphertext and send them to the user.The user obtains the target image with the private key.The experimental results show that the method is feasible and simple.展开更多
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 propose...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.展开更多
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 domai...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.展开更多
基金funded by National Science and Technology Council,Taiwan,grant numbers are 110-2401-H-002-094-MY2 and 112-2221-E-130-001.
文摘As data analysis often incurs significant communication and computational costs,these tasks are increasingly outsourced to cloud computing platforms.However,this introduces privacy concerns,as sensitive data must be transmitted to and processed by untrusted parties.To address this,fully homomorphic encryption(FHE)has emerged as a promising solution for privacy-preserving Machine-Learning-as-a-Service(MLaaS),enabling computation on encrypted data without revealing the plaintext.Nevertheless,FHE remains computationally expensive.As a result,approximate homomorphic encryption(AHE)schemes,such as CKKS,have attracted attention due to their efficiency.In our previous work,we proposed RP-OKC,a CKKS-based clustering scheme implemented via TenSEAL.However,errors inherent to CKKS operations—termed CKKS-errors—can affect the accuracy of the result after decryption.Since these errors can be mitigated through post-decryption rounding,we propose a data pre-scaling technique to increase the number of significant digits and reduce CKKS-errors.Furthermore,we introduce an Operation-Error-Estimation(OEE)table that quantifies upper-bound error estimates for various CKKS operations.This table enables error-aware decryption correction,ensuring alignment between encrypted and plaintext results.We validate our method on K-means clustering using the Kaggle Customer Segmentation dataset.Experimental results confirm that the proposed scheme enhances the accuracy and reliability of privacy-preserving data analysis in cloud environments.
基金supported by the fllowing projects:Natural Science Foundation of China under Grant 62172436Self-Initiated Scientific Research Project of the Chinese People's Armed Police Force under Grant ZZKY20243129Basic Frontier Innovation Project of the Engineering University of the Chinese People's Armed Police Force under Grant WJY202421.
文摘Due to the rapid advancement of information technology,data has emerged as the core resource driving decision-making and innovation across all industries.As the foundation of artificial intelligence,machine learning(ML)has expanded its applications into intelligent recommendation systems,autonomous driving,medical diagnosis,and financial risk assessment.However,it relies on massive datasets,which contain sensitive personal information.Consequently,Privacy-Preserving Machine Learning(PPML)has become a critical research direction.To address the challenges of efficiency and accuracy in encrypted data computation within PPML,Homomorphic Encryption(HE)technology is a crucial solution,owing to its capability to facilitate computations on encrypted data.However,the integration of machine learning and homomorphic encryption technologies faces multiple challenges.Against this backdrop,this paper reviews homomorphic encryption technologies,with a focus on the advantages of the Cheon-Kim-Kim-Song(CKKS)algorithm in supporting approximate floating-point computations.This paper reviews the development of three machine learning techniques:K-nearest neighbors(KNN),K-means clustering,and face recognition-in integration with homomorphic encryption.It proposes feasible schemes for typical scenarios,summarizes limitations and future optimization directions.Additionally,it presents a systematic exploration of the integration of homomorphic encryption and machine learning from the essence of the technology,application implementation,performance trade-offs,technological convergence and future pathways to advance technological development.
基金supported by the Innovation Program for Quantum Science and technology(2021ZD0301300)supported by the Fundamental Research Funds for the Central Universities(Nos.3282024046,3282024052,3282024058,3282023017).
文摘Ciphertext data retrieval in cloud databases suffers from some critical limitations,such as inadequate security measures,disorganized key management practices,and insufficient retrieval access control capabilities.To address these problems,this paper proposes an enhanced Fully Homomorphic Encryption(FHE)algorithm based on an improved DGHV algorithm,coupled with an optimized ciphertext retrieval scheme.Our specific contributions are outlined as follows:First,we employ an authorization code to verify the user’s retrieval authority and perform hierarchical access control on cloud storage data.Second,a triple-key encryption mechanism,which separates the data encryption key,retrieval authorization key,and retrieval key,is designed.Different keys are provided to different entities to run corresponding system functions.The key separation architecture proves particularly advantageous in multi-verifier coexistence scenarios,environments involving untrusted third-party retrieval services.Finally,the enhanced DGHV-based retrieval mechanism extends conventional functionality by enabling multi-keyword queries with similarity-ranked results,thereby significantly improving both the functionality and usability of the FHE system.
基金supported by the National Key Research and Development Plan in China(Grant No.2020YFB1005500)。
文摘With increasing demand for data circulation,ensuring data security and privacy is paramount,specifically protecting privacy while maximizing utility.Blockchain,while decentralized and transparent,faces challenges in privacy protection and data verification,especially for sensitive data.Existing schemes often suffer from inefficiency and high overhead.We propose a privacy protection scheme using BGV homomorphic encryption and Pedersen Secret Sharing.This scheme enables secure computation on encrypted data,with Pedersen sharding and verifying the private key,ensuring data consistency and immutability.The blockchain framework manages key shards,verifies secrets,and aids security auditing.This approach allows for trusted computation without revealing the underlying data.Preliminary results demonstrate the scheme's feasibility in ensuring data privacy and security,making data available but not visible.This study provides an effective solution for data sharing and privacy protection in blockchain applications.
基金supported by the National Key R&D Program of China(No.2017YFB0802000)the Innovative Research Team in Engineering University of PAP(No.KYTD201805)+2 种基金the National Natural Science Foundation of China(No.61872384)the Natural Science Basic Research Plan in Shaanxi Province of China(No.2020JQ-492)the Fundamental Research Project of Engineering University of PAP(Nos.WJY201910,WJY201914,and WJY201912)。
文摘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.
文摘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.
文摘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.
基金supported by ZTE Industry-University-Institute Cooperation Funds under Grant No.IA20230628015the State Key Laboratory of Particle Detection and Electronics under Grant No.SKLPDE-KF-202314.
文摘Digital twin is a novel technology that has achieved significant progress in industrial manufactur-ing systems in recent years.In the digital twin envi-ronment,entities in the virtual space collect data from devices in the physical space to analyze their states.However,since a lot of devices exist in the physical space,the digital twin system needs to aggregate data from multiple devices at the edge gateway.Homomor-phic integrity and confidentiality protections are two important requirements for this data aggregation pro-cess.Unfortunately,existing homomorphic encryp-tion algorithms do not support integrity protection,and existing homomorphic signing algorithms require all signers to use the same signing key,which is not feasible in the digital twin environment.Moreover,for both integrity and confidentiality protections,the homomorphic signing algorithm must be compatible with the aggregation manner of the homomorphic en-cryption algorithm.To address these issues,this paper designs a novel homomorphic aggregation scheme,which allows multiple devices in the physical space to sign different data using different keys and support in-tegrity and confidentiality protections.Finally,the security of the newly designed scheme is analyzed,and its efficiency is evaluated.Experimental results show that our scheme is feasible for real world applications.
基金supported in part by the Sichuan Science and Technology Program(2024YFHZ0015)the Key Laboratory of Data Protection and Intelligent Management,Ministry of Education,Sichuan University(SCUSACXYD202401).
文摘False Data Injection Attack(FDIA),a disruptive cyber threat,is becoming increasingly detrimental to smart grids with the deepening integration of information technology and physical power systems,leading to system unreliability,data integrity loss and operational vulnerability exposure.Given its widespread harm and impact,conducting in-depth research on FDIA detection is vitally important.This paper innovatively introduces a FDIA detection scheme:A Protected Federated Deep Learning(ProFed),which leverages Federated Averaging algorithm(FedAvg)as a foundational framework to fortify data security,harnesses pre-trained enhanced spatial-temporal graph neural networks(STGNN)to perform localized model training and integrates the Cheon-Kim-Kim-Song(CKKS)homomorphic encryption system to secure sensitive information.Simulation tests on IEEE 14-bus and IEEE 118-bus systems demonstrate that our proposed method outperforms other state-of-the-art detection methods across all evaluation metrics,with peak improvements reaching up to 35%.
基金supported by Institute of Information communications Technology Planning Evaluation(IITP)grant funded by theKorea government(MSIT)[No.2022-0-01047,Development of statistical analysis algorithm and module using homomorphic encryption based on real number operation,100%].
文摘In this study,we investigated privacy-preserving ID3 Decision Tree(PPID3)training and inference based on fully homomorphic encryption(FHE),which has not been actively explored due to the high computational cost associated with managing numerous child nodes in an ID3 tree.We propose HEaaN-ID3,a novel approach to realize PPID3 using the Cheon-Kim-Kim-Song(CKKS)scheme.HEaaN-ID3 is the first FHE-based ID3 framework that completes both training and inference without any intermediate decryption,which is especially valuable when decryption keys are inaccessible or a single-cloud security domain is assumed.To enhance computational efficiency,we adopt a modified Gini impurity(MGI)score instead of entropy to evaluate information gain,thereby avoiding costly inverse operations.In addition,we fully leverage the Single Instruction Multiple Data(SIMD)property of CKKS to parallelize computations at multiple tree nodes.Unlike previous approaches that require decryption at each node or rely on two-party secure computation,our method enables a fully non-interactive training and inference pipeline in the encrypted domain.We validated the proposed scheme using UCI datasets with both numerical and nominal features,demonstrating inference accuracy comparable to plaintext implementations in Scikit-Learn.Moreover,experiments show that HEaaN-ID3 significantly reduces training and inference time per node relative to earlier FHE-based approaches.
基金Supported by the National Natural Science Foun-dation of China (90104005)
文摘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.
基金The first author would like to thank for the Fund of Jiangsu Innovation Program for Graduate Education，the Fundamental Research Funds for the Central Universities,and Ningbo Natural Science Foundation，the Chinese National Scholarship fund,and also appreciate the benefit to this work from projects in science and technique of Ningbo municipal.The third author would like to thank for Ningbo Natural Science Foundation
文摘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.
基金Supported by the National 863 Project(2012AA011705)Guangxi Natural Science Foundation(2013GXNSFBB053005)+2 种基金Guangxi Science Research&Technology Development Project(14124004-4-10)Guangdong Natural Science Foundation(2014A030313517)Guangxi Experiment Center of Information Science Foundation
文摘Although the learning with errors（LWE）-based full homomorphic encryption scheme was the first example of deviation from the original Gentry＇s blueprint, the scheme did not give detailed conversion process of circuit layer structure, and must rely on bootstrapping technique to achieve full homomorphism. Therefore, through modifying the re-linearization technique proposed by the above scheme, a technique called non-matrix key switching is presented, which includes key switching with re-linearization and pure key switching. The complex matrix operations of existing key switching technique are removed. Combining this technique with modulus switching, a （leveled） fully homomorphic encryption scheme without bootstrapping from LWE is constructed. In order to make circuit layer structure clear, the scheme gives detailed refresh door operation. Finally, we use bootstrapping to upgrade arithmetic circuit to any layer, and make the homomorphic computing capability of the scheme have nothing to circuit depth.
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The National Basic Research Program of China(973Program)(No.2013CB338003)
文摘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.
基金Supported by the Research Program of Chongqing Education Commission(JK15012027,JK1601225)the Chongqing Research Program of Basic Research and Frontier Technology(cstc2017jcyjBX0008)+1 种基金the Graduate Student Research and Innovation Foundation of Chongqing(CYB17026)the Basic Applied Research Program of Qinghai Province(2019-ZJ-7099)
文摘In order to guarantee the user's privacy and the integrity of data when retrieving ciphertext in an untrusted cloud environment, an improved ciphertext retrieval scheme was proposed based on full homomorphic encryption. This scheme can encrypt two bits one time and improve the efficiency of retrieval. Moreover, it has small key space and reduces the storage space. Meanwhile, the homomorphic property of this scheme was proved in detail. The experimental results and comparisons show that the proposed scheme is characterized by increased security, high efficiency and low cost.
基金the Fundamental Research Funds for the Central Universities(Grant No.2019XDA02)the Scientific Research Foundation of North China University of Technology。
文摘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.
基金This work was supported in part by the Natural Science Foundation of China(No.61772234,61272414).
文摘With the rapid development of information network,the computing resources and storage capacity of ordinary users cannot meet their needs of data processing.The emergence of cloud computing solves this problem but brings data security problems.How to manage and retrieve ciphertext data effectively becomes a challenging problem.To these problems,a new image retrieval method in ciphertext domain by block image encrypting based on Paillier homomophic cryptosystem is proposed in this paper.This can be described as follows:According to the Paillier encryption technology,the image owner encrypts the original image in blocks,obtains the image in ciphertext domain,then passes it to the third party server.The server calculates the difference histogram of the image in ciphertext domain according to the public key and establishes the index database.The user passes the retrieved image to the server.The server computes the differential histogram of the retrieved image by public key.Then,compares the similarity of it with the histogram in index database and selects larger similarity images in ciphertext and send them to the user.The user obtains the target image with the private key.The experimental results show that the method is feasible and simple.
基金This work was supported by the Open Fund of Advanced Cryptography and System Security Key Laboratory of Sichuan Province(Grant No.SKLACSS-202101)NSFC(Grant Nos.62176273,61962009)+3 种基金the Foundation of Guizhou Provincial Key Laboratory of Public Big Data(No.2019BDKFJJ010,2019BDKFJJ014)the Fundamental Re-search Funds for Beijing Municipal Commission of Education,Beijing Urban Governance Re-search Base of North China University of Technology,the Natural Science Foundation of Inner Mongolia(2021MS06006)Baotou Kundulun District Science and technology plan project(YF2020013)Inner Mongolia discipline inspection and supervision big data laboratory open project fund(IMDBD2020020).
文摘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.
文摘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.
