In this paper,we discuss the structure of intuitionistic fuzzy(IF)homomorphisms,exact sequences and some other concepts in category of IF modules.We study on IF exact sequences and IF Hom functors in IFR-Mod and obtai...In this paper,we discuss the structure of intuitionistic fuzzy(IF)homomorphisms,exact sequences and some other concepts in category of IF modules.We study on IF exact sequences and IF Hom functors in IFR-Mod and obtain some results about them.If R is a commutative ring and 0→A~f→B~g→C is an exact sequence in IFR-Mod,where f is IF split homomorphism,then we show that Hom_(IF-R)(D,-)preserves the sequence for every D∈IFR-Mod.Also IF projective modules will be introduced and investigated in this paper.Finally we define product and coproduct of IF modules and show that if M is an R-module,A=(μ_(A),ν_(A))≤_(IF)M and e_(i)∈E(R)for any i∈I,then Hom(Пi2I 0IF Rei;A)=Πi2I Hom(0IF Rei;A).展开更多
逻辑回归是一种广泛应用于现实分类任务的机器学习模型。随着数据孤岛问题的涌现,如何针对多参与主体非贯通数据联合构建逻辑回归模型成为一个关键问题。纵向联邦学习可实现数据明文不暴露前提下多主体跨样本特征的联合机器学习模型训...逻辑回归是一种广泛应用于现实分类任务的机器学习模型。随着数据孤岛问题的涌现,如何针对多参与主体非贯通数据联合构建逻辑回归模型成为一个关键问题。纵向联邦学习可实现数据明文不暴露前提下多主体跨样本特征的联合机器学习模型训练。然而,现有纵向联邦逻辑回归方法主要基于同态加密技术,具有计算和通信开销大的短板。针对逻辑回归模型,研究安全高效的纵向联邦学习算法,目标实现数据隐私保护和模型学习效率的较优权衡。具体地,基于秘密共享提出了一种面向逻辑回归模型的高效率纵向联邦学习算法(Vertical Federated Logistic Regression algorithm based on Secret Sharing, VFLR-SS),通过将跨域分析过程中的中间数据随机分解为多个秘密份额进行交互从而实现隐私保护,同时避免了同态加密引发的计算和通信开销。对VFLR-SS的安全性进行了分析,并基于真实数据对算法进行了验证。实验结果表明VFLR-SS可实现与集中式逻辑回归算法可比的效用和性能,大幅降低了传统同态加密方法中的计算及通信开销。展开更多
The next-generation RAN,known as Open Radio Access Network(ORAN),allows for several advantages,including cost-effectiveness,network flexibility,and interoperability.Now ORAN applications,utilising machine learning(ML)...The next-generation RAN,known as Open Radio Access Network(ORAN),allows for several advantages,including cost-effectiveness,network flexibility,and interoperability.Now ORAN applications,utilising machine learning(ML)and artificial intelligence(AI)techniques,have become standard practice.The need for Federated Learning(FL)for ML model training in ORAN environments is heightened by the modularised structure of the ORAN architecture and the shortcomings of conventional ML techniques.However,the traditional plaintext model update sharing of FL in multi-BS contexts is susceptible to privacy violations such as deep-leakage gradient assaults and inference.Therefore,this research presents a novel blockchain-assisted improved cryptographic privacy-preserving federated learning(BICPPFL)model,with the help of ORAN,to safely carry out federated learning and protect privacy.This model improves on the conventional masking technique for sharing model parameters by adding new characteristics.These features include the choice of distributed aggregators,validation for final model aggregation,and individual validation for BSs.To manage the security and privacy of FL processes,a combined homomorphic proxy-reencryption(HPReE)and lattice-cryptographic method(HPReEL)has been used.The upgraded delegated proof of stake(Up-DPoS)consensus protocol,which will provide quick validation of model exchanges and protect against malicious attacks,is employed for effective consensus across blockchain nodes.Without sacrificing performance metrics,the BICPPFL model strengthens privacy and adds security layers while facilitating the transfer of sensitive data across several BSs.The framework is deployed on top of a Hyperledger Fabric blockchain to evaluate its effectiveness.The experimental findings prove the reliability and privacy-preserving capability of the BICPPFL model.展开更多
With the rapid development of the Artificial Intelligence of Things(AIoT),convolutional neural networks(CNNs)have demonstrated potential and remarkable performance in AIoT applications due to their excellent performan...With the rapid development of the Artificial Intelligence of Things(AIoT),convolutional neural networks(CNNs)have demonstrated potential and remarkable performance in AIoT applications due to their excellent performance in various inference tasks.However,the users have concerns about privacy leakage for the use of AI and the performance and efficiency of computing on resource-constrained IoT edge devices.Therefore,this paper proposes an efficient privacy-preserving CNN framework(i.e.,EPPA)based on the Fully Homomorphic Encryption(FHE)scheme for AIoT application scenarios.In the plaintext domain,we verify schemes with different activation structures to determine the actual activation functions applicable to the corresponding ciphertext domain.Within the encryption domain,we integrate batch normalization(BN)into the convolutional layers to simplify the computation process.For nonlinear activation functions,we use composite polynomials for approximate calculation.Regarding the noise accumulation caused by homomorphic multiplication operations,we realize the refreshment of ciphertext noise through minimal“decryption-encryption”interactions,instead of adopting bootstrapping operations.Additionally,in practical implementation,we convert three-dimensional convolution into two-dimensional convolution to reduce the amount of computation in the encryption domain.Finally,we conduct extensive experiments on four IoT datasets,different CNN architectures,and two platforms with different resource configurations to evaluate the performance of EPPA in detail.展开更多
The advent of quantum computing poses a significant challenge to traditional cryptographic protocols,particularly those used in SecureMultiparty Computation(MPC),a fundamental cryptographic primitive for privacypreser...The advent of quantum computing poses a significant challenge to traditional cryptographic protocols,particularly those used in SecureMultiparty Computation(MPC),a fundamental cryptographic primitive for privacypreserving computation.Classical MPC relies on cryptographic techniques such as homomorphic encryption,secret sharing,and oblivious transfer,which may become vulnerable in the post-quantum era due to the computational power of quantum adversaries.This study presents a review of 140 peer-reviewed articles published between 2000 and 2025 that used different databases like MDPI,IEEE Explore,Springer,and Elsevier,examining the applications,types,and security issues with the solution of Quantum computing in different fields.This review explores the impact of quantum computing on MPC security,assesses emerging quantum-resistant MPC protocols,and examines hybrid classicalquantum approaches aimed at mitigating quantum threats.We analyze the role of Quantum Key Distribution(QKD),post-quantum cryptography(PQC),and quantum homomorphic encryption in securing multiparty computations.Additionally,we discuss the challenges of scalability,computational efficiency,and practical deployment of quantumsecure MPC frameworks in real-world applications such as privacy-preserving AI,secure blockchain transactions,and confidential data analysis.This review provides insights into the future research directions and open challenges in ensuring secure,scalable,and quantum-resistant multiparty computation.展开更多
文摘In this paper,we discuss the structure of intuitionistic fuzzy(IF)homomorphisms,exact sequences and some other concepts in category of IF modules.We study on IF exact sequences and IF Hom functors in IFR-Mod and obtain some results about them.If R is a commutative ring and 0→A~f→B~g→C is an exact sequence in IFR-Mod,where f is IF split homomorphism,then we show that Hom_(IF-R)(D,-)preserves the sequence for every D∈IFR-Mod.Also IF projective modules will be introduced and investigated in this paper.Finally we define product and coproduct of IF modules and show that if M is an R-module,A=(μ_(A),ν_(A))≤_(IF)M and e_(i)∈E(R)for any i∈I,then Hom(Пi2I 0IF Rei;A)=Πi2I Hom(0IF Rei;A).
文摘逻辑回归是一种广泛应用于现实分类任务的机器学习模型。随着数据孤岛问题的涌现,如何针对多参与主体非贯通数据联合构建逻辑回归模型成为一个关键问题。纵向联邦学习可实现数据明文不暴露前提下多主体跨样本特征的联合机器学习模型训练。然而,现有纵向联邦逻辑回归方法主要基于同态加密技术,具有计算和通信开销大的短板。针对逻辑回归模型,研究安全高效的纵向联邦学习算法,目标实现数据隐私保护和模型学习效率的较优权衡。具体地,基于秘密共享提出了一种面向逻辑回归模型的高效率纵向联邦学习算法(Vertical Federated Logistic Regression algorithm based on Secret Sharing, VFLR-SS),通过将跨域分析过程中的中间数据随机分解为多个秘密份额进行交互从而实现隐私保护,同时避免了同态加密引发的计算和通信开销。对VFLR-SS的安全性进行了分析,并基于真实数据对算法进行了验证。实验结果表明VFLR-SS可实现与集中式逻辑回归算法可比的效用和性能,大幅降低了传统同态加密方法中的计算及通信开销。
文摘The next-generation RAN,known as Open Radio Access Network(ORAN),allows for several advantages,including cost-effectiveness,network flexibility,and interoperability.Now ORAN applications,utilising machine learning(ML)and artificial intelligence(AI)techniques,have become standard practice.The need for Federated Learning(FL)for ML model training in ORAN environments is heightened by the modularised structure of the ORAN architecture and the shortcomings of conventional ML techniques.However,the traditional plaintext model update sharing of FL in multi-BS contexts is susceptible to privacy violations such as deep-leakage gradient assaults and inference.Therefore,this research presents a novel blockchain-assisted improved cryptographic privacy-preserving federated learning(BICPPFL)model,with the help of ORAN,to safely carry out federated learning and protect privacy.This model improves on the conventional masking technique for sharing model parameters by adding new characteristics.These features include the choice of distributed aggregators,validation for final model aggregation,and individual validation for BSs.To manage the security and privacy of FL processes,a combined homomorphic proxy-reencryption(HPReE)and lattice-cryptographic method(HPReEL)has been used.The upgraded delegated proof of stake(Up-DPoS)consensus protocol,which will provide quick validation of model exchanges and protect against malicious attacks,is employed for effective consensus across blockchain nodes.Without sacrificing performance metrics,the BICPPFL model strengthens privacy and adds security layers while facilitating the transfer of sensitive data across several BSs.The framework is deployed on top of a Hyperledger Fabric blockchain to evaluate its effectiveness.The experimental findings prove the reliability and privacy-preserving capability of the BICPPFL model.
基金supported by the Natural Science Foundation of China No.62362008the Major Scientific and Technological Special Project of Guizhou Province([2024]014).
文摘With the rapid development of the Artificial Intelligence of Things(AIoT),convolutional neural networks(CNNs)have demonstrated potential and remarkable performance in AIoT applications due to their excellent performance in various inference tasks.However,the users have concerns about privacy leakage for the use of AI and the performance and efficiency of computing on resource-constrained IoT edge devices.Therefore,this paper proposes an efficient privacy-preserving CNN framework(i.e.,EPPA)based on the Fully Homomorphic Encryption(FHE)scheme for AIoT application scenarios.In the plaintext domain,we verify schemes with different activation structures to determine the actual activation functions applicable to the corresponding ciphertext domain.Within the encryption domain,we integrate batch normalization(BN)into the convolutional layers to simplify the computation process.For nonlinear activation functions,we use composite polynomials for approximate calculation.Regarding the noise accumulation caused by homomorphic multiplication operations,we realize the refreshment of ciphertext noise through minimal“decryption-encryption”interactions,instead of adopting bootstrapping operations.Additionally,in practical implementation,we convert three-dimensional convolution into two-dimensional convolution to reduce the amount of computation in the encryption domain.Finally,we conduct extensive experiments on four IoT datasets,different CNN architectures,and two platforms with different resource configurations to evaluate the performance of EPPA in detail.
文摘The advent of quantum computing poses a significant challenge to traditional cryptographic protocols,particularly those used in SecureMultiparty Computation(MPC),a fundamental cryptographic primitive for privacypreserving computation.Classical MPC relies on cryptographic techniques such as homomorphic encryption,secret sharing,and oblivious transfer,which may become vulnerable in the post-quantum era due to the computational power of quantum adversaries.This study presents a review of 140 peer-reviewed articles published between 2000 and 2025 that used different databases like MDPI,IEEE Explore,Springer,and Elsevier,examining the applications,types,and security issues with the solution of Quantum computing in different fields.This review explores the impact of quantum computing on MPC security,assesses emerging quantum-resistant MPC protocols,and examines hybrid classicalquantum approaches aimed at mitigating quantum threats.We analyze the role of Quantum Key Distribution(QKD),post-quantum cryptography(PQC),and quantum homomorphic encryption in securing multiparty computations.Additionally,we discuss the challenges of scalability,computational efficiency,and practical deployment of quantumsecure MPC frameworks in real-world applications such as privacy-preserving AI,secure blockchain transactions,and confidential data analysis.This review provides insights into the future research directions and open challenges in ensuring secure,scalable,and quantum-resistant multiparty computation.
