The wide application of smart contracts allows industry companies to implement some complex distributed collaborative businesses,which involve the calculation of complex functions,such as matrix operations.However,com...The wide application of smart contracts allows industry companies to implement some complex distributed collaborative businesses,which involve the calculation of complex functions,such as matrix operations.However,complex functions such as matrix operations are difficult to implement on Ethereum Virtual Machine(EVM)-based smart contract platforms due to their distributed security environment limitations.Existing off-chain methods often result in a significant reduction in contract execution efficiency,thus a platform software development kit interface implementation method has become a feasible way to reduce overheads,but this method cannot verify operation correctness and may leak sensitive user data.To solve the above problems,we propose a verifiable EVM-based smart contract cross-language implementation scheme for complex operations,especially matrix operations,which can guarantee operation correctness and user privacy while ensuring computational efficiency.In this scheme,a verifiable interaction process is designed to verify the computation process and results,and a matrix blinding technology is introduced to protect sensitive user data in the calculation process.The security analysis and performance tests show that the proposed scheme can satisfy the correctness and privacy of the cross-language implementation of smart contracts at a small additional efficiency cost.展开更多
In recent years,the field of higher education in China has put forward clear requirements for the construction of ideological and political education in courses.Accounting English,as an important course for cultivatin...In recent years,the field of higher education in China has put forward clear requirements for the construction of ideological and political education in courses.Accounting English,as an important course for cultivating international accounting talents,urgently needs to integrate professional ethics and national consciousness into professional teaching[1].In response to the lack of a professional reference system for ideological and political education in accounting English courses,this paper,guided by the OBE educational concept,constructs a three-dimensional objective matrix model based on the international Certified Public Accountant(CPA)competency framework.By deconstructing the IFAC professional competence standards,a mapping mechanism of“professional competence-language carrier-ideological and political content”is proposed.展开更多
Distributed data fusion is essential for numerous applications,yet faces significant privacy security challenges.Federated learning(FL),as a distributed machine learning paradigm,offers enhanced data privacy protectio...Distributed data fusion is essential for numerous applications,yet faces significant privacy security challenges.Federated learning(FL),as a distributed machine learning paradigm,offers enhanced data privacy protection and has attracted widespread attention.Consequently,research increasingly focuses on developing more secure FL techniques.However,in real-world scenarios involving malicious entities,the accuracy of FL results is often compromised,particularly due to the threat of collusion between two servers.To address this challenge,this paper proposes an efficient and verifiable data aggregation protocol with enhanced privacy protection.After analyzing attack methods against prior schemes,we implement key improvements.Specifically,by incorporating cascaded random numbers and perturbation terms into gradients,we strengthen the privacy protection afforded by polynomial masking,effectively preventing information leakage.Furthermore,our protocol features an enhanced verification mechanism capable of detecting collusive behaviors between two servers.Accuracy testing on the MNIST and CIFAR-10 datasets demonstrates that our protocol maintains accuracy comparable to the Federated Averaging Algorithm.In scheme efficiency comparisons,while incurring only a marginal increase in verification overhead relative to the baseline scheme,our protocol achieves an average improvement of 93.13% in privacy protection and verification overhead compared to the state-of-the-art scheme.This result highlights its optimal balance between overall overhead and functionality.A current limitation is that the verificationmechanismcannot precisely pinpoint the source of anomalies within aggregated results when server-side malicious behavior occurs.Addressing this limitation will be a focus of future research.展开更多
Federated Learning(FL)has emerged as a promising distributed machine learning paradigm that enables multi-party collaborative training while eliminating the need for raw data sharing.However,its reliance on a server i...Federated Learning(FL)has emerged as a promising distributed machine learning paradigm that enables multi-party collaborative training while eliminating the need for raw data sharing.However,its reliance on a server introduces critical security vulnerabilities:malicious servers can infer private information from received local model updates or deliberately manipulate aggregation results.Consequently,achieving verifiable aggregation without compromising client privacy remains a critical challenge.To address these problem,we propose a reversible data hiding in encrypted domains(RDHED)scheme,which designs joint secret message embedding and extraction mechanism.This approach enables clients to embed secret messages into ciphertext redundancy spaces generated during model encryption.During the server aggregation process,the embedded messages from all clients fuse within the ciphertext space to form a joint embedding message.Subsequently,clients can decrypt the aggregated results and extract this joint embedding message for verification purposes.Building upon this foundation,we integrate the proposed RDHED scheme with linear homomorphic hash and digital signatures to design a verifiable privacy-preserving aggregation protocol for single-server architectures(VPAFL).Theoretical proofs and experimental analyses show that VPAFL can effectively protect user privacy,achieve lightweight computational and communication overhead of users for verification,and present significant advantages with increasing model dimension.展开更多
In traditional secret image sharing schemes,a secret image is shared among shareholders who have the same position.But if the shareholders have two different positions,essential and non‐essential,it is necessary to u...In traditional secret image sharing schemes,a secret image is shared among shareholders who have the same position.But if the shareholders have two different positions,essential and non‐essential,it is necessary to use essential secret image sharing schemes.In this article,a verifiable essential secret image sharing scheme based on HLRs is proposed.Shareholder's share consists of two parts.The first part is produced by the shareholders,which prevents the fraud of dealers.The second part is a shadow image that is produced by using HLRs and the first part of share.The verification of the first part of the shares is done for the first time by using multilinear and bilinear maps.Also,for verifying shadow images,Bloom Filters are used for the first time.The proposed scheme is more efficient than similar schemes,and for the first part of the shares,has formal security.展开更多
With the recent technological developments,massive vehicular ad hoc networks(VANETs)have been established,enabling numerous vehicles and their respective Road Side Unit(RSU)components to communicate with oneanother.Th...With the recent technological developments,massive vehicular ad hoc networks(VANETs)have been established,enabling numerous vehicles and their respective Road Side Unit(RSU)components to communicate with oneanother.The best way to enhance traffic flow for vehicles and traffic management departments is to share thedata they receive.There needs to be more protection for the VANET systems.An effective and safe methodof outsourcing is suggested,which reduces computation costs by achieving data security using a homomorphicmapping based on the conjugate operation of matrices.This research proposes a VANET-based data outsourcingsystem to fix the issues.To keep data outsourcing secure,the suggested model takes cryptography models intoaccount.Fog will keep the generated keys for the purpose of vehicle authentication.For controlling and overseeingthe outsourced data while preserving privacy,the suggested approach considers the Trusted Certified Auditor(TCA).Using the secret key,TCA can identify the genuine identity of VANETs when harmful messages aredetected.The proposed model develops a TCA-based unique static vehicle labeling system using cryptography(TCA-USVLC)for secure data outsourcing and privacy preservation in VANETs.The proposed model calculatesthe trust of vehicles in 16 ms for an average of 180 vehicles and achieves 98.6%accuracy for data encryption toprovide security.The proposedmodel achieved 98.5%accuracy in data outsourcing and 98.6%accuracy in privacypreservation in fog-enabled VANETs.Elliptical curve cryptography models can be applied in the future for betterencryption and decryption rates with lightweight cryptography operations.展开更多
针对现有用户协作算法存在共谋攻击、背景知识攻击以及用户协作意愿等问题,基于可验证秘密共享与智能合约提出了一种用户协作隐私保护算法(privacy protection algorithm based on verifiable secret sharing and smart contracts,VSS-S...针对现有用户协作算法存在共谋攻击、背景知识攻击以及用户协作意愿等问题,基于可验证秘密共享与智能合约提出了一种用户协作隐私保护算法(privacy protection algorithm based on verifiable secret sharing and smart contracts,VSS-SCPPA)。该算法首先利用可验证秘密共享算法对用户查询信息进行加密和分裂,并提供系数承诺以验证子秘密数据的完整性。其次,结合智能合约与差分隐私技术设计了一种用户选择算法,构建匿名集。对该算法在抵御串通攻击方面的有效性进行了分析。通过在Geolife与BerlinMOD数据集上的实验,结果显示VSS-SCPPA的隐私保护性更高。与Tr-privacy、Ik-anonymity和GCS相比,VSS-SCPPA的效率分别提高了约86.34%、99.27%和99.19%。VSS-SCPPA在提高隐私保护性的同时显著提升了算法效率,证明了其在用户协作隐私保护中的有效性。展开更多
基金supported in part by the National Natural Science Foundation of China under Grant 62272007,U23B2002in part by the Excellent Young Talents Project of the Beijing Municipal University Teacher Team Construction Support Plan under Grant BPHR202203031+1 种基金in part by the Yunnan Key Laboratory of Blockchain Application Technology under Grant 2021105AG070005(YNB202102)in part by the Open Topics of Key Laboratory of Blockchain Technology and Data Security,The Ministry of Industry and Information Technology of the People’s Republic of China under Grant 20243222。
文摘The wide application of smart contracts allows industry companies to implement some complex distributed collaborative businesses,which involve the calculation of complex functions,such as matrix operations.However,complex functions such as matrix operations are difficult to implement on Ethereum Virtual Machine(EVM)-based smart contract platforms due to their distributed security environment limitations.Existing off-chain methods often result in a significant reduction in contract execution efficiency,thus a platform software development kit interface implementation method has become a feasible way to reduce overheads,but this method cannot verify operation correctness and may leak sensitive user data.To solve the above problems,we propose a verifiable EVM-based smart contract cross-language implementation scheme for complex operations,especially matrix operations,which can guarantee operation correctness and user privacy while ensuring computational efficiency.In this scheme,a verifiable interaction process is designed to verify the computation process and results,and a matrix blinding technology is introduced to protect sensitive user data in the calculation process.The security analysis and performance tests show that the proposed scheme can satisfy the correctness and privacy of the cross-language implementation of smart contracts at a small additional efficiency cost.
文摘In recent years,the field of higher education in China has put forward clear requirements for the construction of ideological and political education in courses.Accounting English,as an important course for cultivating international accounting talents,urgently needs to integrate professional ethics and national consciousness into professional teaching[1].In response to the lack of a professional reference system for ideological and political education in accounting English courses,this paper,guided by the OBE educational concept,constructs a three-dimensional objective matrix model based on the international Certified Public Accountant(CPA)competency framework.By deconstructing the IFAC professional competence standards,a mapping mechanism of“professional competence-language carrier-ideological and political content”is proposed.
基金supported by National Key R&D Program of China(2023YFB3106100)National Natural Science Foundation of China(62102452,62172436)Natural Science Foundation of Shaanxi Province(2023-JCYB-584).
文摘Distributed data fusion is essential for numerous applications,yet faces significant privacy security challenges.Federated learning(FL),as a distributed machine learning paradigm,offers enhanced data privacy protection and has attracted widespread attention.Consequently,research increasingly focuses on developing more secure FL techniques.However,in real-world scenarios involving malicious entities,the accuracy of FL results is often compromised,particularly due to the threat of collusion between two servers.To address this challenge,this paper proposes an efficient and verifiable data aggregation protocol with enhanced privacy protection.After analyzing attack methods against prior schemes,we implement key improvements.Specifically,by incorporating cascaded random numbers and perturbation terms into gradients,we strengthen the privacy protection afforded by polynomial masking,effectively preventing information leakage.Furthermore,our protocol features an enhanced verification mechanism capable of detecting collusive behaviors between two servers.Accuracy testing on the MNIST and CIFAR-10 datasets demonstrates that our protocol maintains accuracy comparable to the Federated Averaging Algorithm.In scheme efficiency comparisons,while incurring only a marginal increase in verification overhead relative to the baseline scheme,our protocol achieves an average improvement of 93.13% in privacy protection and verification overhead compared to the state-of-the-art scheme.This result highlights its optimal balance between overall overhead and functionality.A current limitation is that the verificationmechanismcannot precisely pinpoint the source of anomalies within aggregated results when server-side malicious behavior occurs.Addressing this limitation will be a focus of future research.
基金supported in part by the National Natural Science Foundation of China under Grants 62102450,62272478the Independent Research Project of a Certain Unit under Grant ZZKY20243127.
文摘Federated Learning(FL)has emerged as a promising distributed machine learning paradigm that enables multi-party collaborative training while eliminating the need for raw data sharing.However,its reliance on a server introduces critical security vulnerabilities:malicious servers can infer private information from received local model updates or deliberately manipulate aggregation results.Consequently,achieving verifiable aggregation without compromising client privacy remains a critical challenge.To address these problem,we propose a reversible data hiding in encrypted domains(RDHED)scheme,which designs joint secret message embedding and extraction mechanism.This approach enables clients to embed secret messages into ciphertext redundancy spaces generated during model encryption.During the server aggregation process,the embedded messages from all clients fuse within the ciphertext space to form a joint embedding message.Subsequently,clients can decrypt the aggregated results and extract this joint embedding message for verification purposes.Building upon this foundation,we integrate the proposed RDHED scheme with linear homomorphic hash and digital signatures to design a verifiable privacy-preserving aggregation protocol for single-server architectures(VPAFL).Theoretical proofs and experimental analyses show that VPAFL can effectively protect user privacy,achieve lightweight computational and communication overhead of users for verification,and present significant advantages with increasing model dimension.
文摘In traditional secret image sharing schemes,a secret image is shared among shareholders who have the same position.But if the shareholders have two different positions,essential and non‐essential,it is necessary to use essential secret image sharing schemes.In this article,a verifiable essential secret image sharing scheme based on HLRs is proposed.Shareholder's share consists of two parts.The first part is produced by the shareholders,which prevents the fraud of dealers.The second part is a shadow image that is produced by using HLRs and the first part of share.The verification of the first part of the shares is done for the first time by using multilinear and bilinear maps.Also,for verifying shadow images,Bloom Filters are used for the first time.The proposed scheme is more efficient than similar schemes,and for the first part of the shares,has formal security.
文摘With the recent technological developments,massive vehicular ad hoc networks(VANETs)have been established,enabling numerous vehicles and their respective Road Side Unit(RSU)components to communicate with oneanother.The best way to enhance traffic flow for vehicles and traffic management departments is to share thedata they receive.There needs to be more protection for the VANET systems.An effective and safe methodof outsourcing is suggested,which reduces computation costs by achieving data security using a homomorphicmapping based on the conjugate operation of matrices.This research proposes a VANET-based data outsourcingsystem to fix the issues.To keep data outsourcing secure,the suggested model takes cryptography models intoaccount.Fog will keep the generated keys for the purpose of vehicle authentication.For controlling and overseeingthe outsourced data while preserving privacy,the suggested approach considers the Trusted Certified Auditor(TCA).Using the secret key,TCA can identify the genuine identity of VANETs when harmful messages aredetected.The proposed model develops a TCA-based unique static vehicle labeling system using cryptography(TCA-USVLC)for secure data outsourcing and privacy preservation in VANETs.The proposed model calculatesthe trust of vehicles in 16 ms for an average of 180 vehicles and achieves 98.6%accuracy for data encryption toprovide security.The proposedmodel achieved 98.5%accuracy in data outsourcing and 98.6%accuracy in privacypreservation in fog-enabled VANETs.Elliptical curve cryptography models can be applied in the future for betterencryption and decryption rates with lightweight cryptography operations.
文摘针对现有用户协作算法存在共谋攻击、背景知识攻击以及用户协作意愿等问题,基于可验证秘密共享与智能合约提出了一种用户协作隐私保护算法(privacy protection algorithm based on verifiable secret sharing and smart contracts,VSS-SCPPA)。该算法首先利用可验证秘密共享算法对用户查询信息进行加密和分裂,并提供系数承诺以验证子秘密数据的完整性。其次,结合智能合约与差分隐私技术设计了一种用户选择算法,构建匿名集。对该算法在抵御串通攻击方面的有效性进行了分析。通过在Geolife与BerlinMOD数据集上的实验,结果显示VSS-SCPPA的隐私保护性更高。与Tr-privacy、Ik-anonymity和GCS相比,VSS-SCPPA的效率分别提高了约86.34%、99.27%和99.19%。VSS-SCPPA在提高隐私保护性的同时显著提升了算法效率,证明了其在用户协作隐私保护中的有效性。
