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A verifiable essential secret image sharing scheme based on HLRs(VESIS-(t,s,k,n))

CAAI Transactions on Intelligence Technology 2024年第2期9卷 388-410页

作者： Massoud Hadian Dehkordi Seyed Taghi Farahi Samaneh Mashhadi Cryptography and Data Security Laboratory School of MathematicsIran University of Science and TechnologyNarmakTehranIran

In traditional secret image sharing schemes,a secret image is shared among shareholders who have the same *** if the shareholders have two different positions,essential and non‐essential,it is necessary to use essential secret image sharing *** this article,a verifiable essential secret image sharing scheme based on HLRs is ***'s share consists of two *** first part is produced by the shareholders,which prevents the fraud of *** second part is a shadow image that is produced by using HLRs and the first part of *** verification of the first part of the shares is done for the first time by using multilinear and bilinear ***,for verifying shadow images,Bloom Filters are used for the first *** proposed scheme is more efficient than similar schemes,and for the first part of the shares,has formal security.

关键词： Bloom Filter cryptography Essential Secret Image Sharing Verifiability

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Integer wavelet transform-based secret image sharing using rook polynomial and hamming code with authentication

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1435-1450页

作者： Sara Charoghchi Zahra Saeidi Samaneh Mashhadi Cryptography and Data Security Laboratory School of Mathematics and Computer ScienceIran University of Science&TechnologyTehranIran

As an effective way to securely transfer secret images,secret image sharing(SIS)has been a noteworthy area of *** in a SIS scheme,a secret image is shared via shadows and could be reconstructed by having the required number of them.A major downside of this method is its noise-like shadows,which draw the malicious users'*** order to overcome this problem,SIS schemes with meaningful shadows are introduced in which the shadows are first hidden in innocent-looking cover images and then *** most of these schemes,the cover image cannot be recovered without distortion,which makes them useless in case of utilising critical cover images such as military or medical ***,embedding the secret data in Least significant bits of the cover image,in many of these schemes,makes them very fragile to steganlysis.A reversible IWT-based SIS scheme using Rook polynomial and Hamming code with authentication is *** order to make the scheme robust to steganalysis,the shadow image is embedded in coefficients of Integer wavelet transform of the cover *** Rook polynomial makes the scheme more secure and moreover makes authentication very easy and with no need to share private key to ***,utilising Hamming code lets us embed data with much less required modifications on the cover image which results in high-quality stego images.

关键词： hamming code homogeneous linear recursion IWT rook polynomial (t n)-secret image sharing

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Blockchain and signcryption enabled asynchronous federated learning framework in fog computing

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Digital Communications and Networks 2025年第2期11卷 442-454页

作者： Zhou Zhou Youliang Tian Jinbo Xiong Changgen Peng Jing Li Nan Yang State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber SecurityFujian Normal UniversityFuzhou 350117China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic TechnologyGuilin 541004China

Federated learning combines with fog computing to transform data sharing into model sharing,which solves the issues of data isolation and privacy disclosure in fog ***,existing studies focus on centralized single-layer aggregation federated learning architecture,which lack the consideration of cross-domain and asynchronous robustness of federated learning,and rarely integrate verification mechanisms from the perspective of *** address the above challenges,we propose a Blockchain and Signcryption enabled Asynchronous Federated Learning(BSAFL)framework based on dual aggregation for cross-domain *** particular,we first design two types of signcryption schemes to secure the interaction and access control of collaborative learning between ***,we construct a differential privacy approach that adaptively adjusts privacy budgets to ensure data privacy and local models'availability of intra-domain ***,we propose an asynchronous aggregation solution that incorporates consensus verification and elastic participation using ***,security analysis demonstrates the security and privacy effectiveness of BSAFL,and the evaluation on real datasets further validates the high model accuracy and performance of BSAFL.

关键词： Blockchain Signcryption Federated learning Asynchronous Fog computing

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Enhanced Privacy-Preserving WiFi Fingerprint Localization from CL Encryption

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Chinese Journal of Electronics 2024年第6期33卷 1435-1446页

作者： Zhiwei WANG Qiuchi ZHU Zhenqi ZHANG School of Computer Nanjing University of Posts and Telecommunications State Key Laboratory of Public Big Data Guizhou University Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu University of Information Engineering Key Laboratory of Computational Science and Application of Hainan Province Hainan Normal University

The WiFi fingerprint-based localization method is considered one of the most popular techniques for indoor localization. In INFOCOM'14, Li et al. proposed a wireless fidelity(WiFi) fingerprint localization system based on Paillier encryption, which is claimed to protect both client C 's location privacy and service provider S's database privacy. However, Yang et al. presented a practical data privacy attack in INFOCOM'18, which allows a polynomial time attacker to obtain S's database. We propose a novel WiFi fingerprint localization system based on CastagnosLaguillaumie(CL) encryption, which has a trustless setup and is efficient due to the excellent properties of CL encryption. To prevent Yang et al.'s attack, the system requires that S selects only the locations from its database that can receive the nonzero signals from all the available access points in C 's nonzero fingerprint in order to determine C's location. security analysis shows that our scheme is secure under Li et al.'s threat model. Furthermore, to enhance the security level of privacy-preserving WiFi fingerprint localization scheme based on CL encryption, we propose a secure and efficient zero-knowledge proof protocol for the discrete logarithm relations in C's encrypted localization queries.

关键词： Location awareness Threat modeling data privacy Protocols databases Fingerprint recognition Trustless services Encryption Servers Wireless fidelity

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DBKEM-AACS: a distributed key escrow model in blockchain with anonymous authentication and committee selection

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Science China(Information Sciences) 2023年第3期66卷 289-290页

作者： Axin XIANG Hongfeng GAO Youliang TIAN Liang WAN College of Computer Science and Technology Guizhou University Institute of Cryptography and Data Security Guizhou University Network and Information Management Center Guizhou University State Key Laboratory of Public Big Data Guizhou University Institute of Computer Software and Theory Guizhou University

Dear editor, Key security is of great practical significance and demand to guarantee the security of digital assets in the blockchain system. At present, users prefer to escrow their assets on centralized institutions, but this phenomenon has gone against the unique characteristics of decentralization and anonymity in the blockchain. Among them, the suspense incidents of assets lock or lost are enough to prove that the security of escrowed keys on the exchanges is questionable.

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Vertical Federated Learning Based on Consortium Blockchain for data Sharing in Mobile Edge Computing

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Computer Modeling in Engineering & Sciences 2023年第10期137卷 345-361页

作者： Yonghao Zhang Yongtang Wu Tao Li Hui Zhou Yuling Chen State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China Blockchain Laboratory of Agricultural Vegetables Weifang University of Science and TechnologyWeifang262700China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic TechnologyGuilin541004China

The data in Mobile Edge Computing(MEC)contains tremendousmarket value,and data sharing canmaximize the usefulness of the ***,certain data is quite sensitive,and sharing it directly may violate *** Federated Learning(VFL)is a secure distributed machine learning framework that completes joint model training by passing encryptedmodel parameters rather than raw data,so there is no data privacy leakage during the training ***,the VFL can build a bridge between data demander and owner to realize data sharing while protecting data ***,the VFL requires a third party for key distribution and decryption of training *** this article,we employ the consortium blockchain instead of the traditional third party and design a VFL architecture based on the consortium blockchain for data sharing in *** specifically,we propose a V-Raft consensus algorithm based on Verifiable Random Functions(VRFs),which is a variant of the *** VRaft is able to elect leader quickly and stably to assist data demander and owner to complete data sharing by ***,we apply secret sharing todistribute the private key to avoid the situationwhere the training result cannot be decrypted if the leader ***,we analyzed the performance of the V-Raft and carried out simulation experiments,and the results show that compared with Raft,the V-Raft has higher efficiency and better scalability.

关键词： Mobile edge computing vertical federated learning consortium blockchain consensus algorithm

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An insider user authentication method based on improved temporal convolutional network

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High-Confidence Computing 2023年第4期3卷 87-95页

作者： Xiaoling Tao Yuelin Yu Lianyou Fu Jianxiang Liu Yunhao Zhang Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic TechnologyGuilin 541004China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic TechnologyGuilin 541004China

With the rapid development of information technology,information system security and insider threat detection have become important topics for organizational *** the current network environment,user behavioral bio-data presents the characteristics of nonlinearity and temporal *** of the existing research on authentication based on user behavioral biometrics adopts the method of manual feature *** do not adequately capture the nonlinear and time-sequential dependencies of behavioral bio-data,and also do not adequately reflect the personalized usage characteristics of users,leading to bottlenecks in the performance of the authentication *** order to solve the above problems,this paper proposes a Temporal Convolutional Network method based on an Efficient Channel Attention mechanism(ECA-TCN)to extract user mouse dynamics features and constructs an one-class Support Vector Machine(OCSVM)for each user for *** results show that compared with four existing deep learning algorithms,the method retains more adequate key information and improves the classification performance of the neural *** the final authentication,the Area Under the Curve(AUC)can reach 96%.

关键词： Insider users Mouse dynamics Feature extraction Temporal convolutional network Efficient channel attention mechanism Authentication

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Differential Privacy Face Image Publishing Method Based on Neighboring Pixels Merging 21

Differential Privacy Face Image Publishing Method Based on N...

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21st International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Lang, Ming Yongli, Wang Yuanyuan, Huang Junyu, Chen School of Cybersecurity Xin Gu Industrial College Chengdu University of Information Technology Chengdu610225 China Dazhou Key Laboratory of Government Data Security Dazhou635000 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China

ISBN: (纸本)9798331519254

Face images contain rich and sensitive personal information. Direct release of facial images on the Internet may lead to the disclosure of personal information. In order to protect the privacy information in facial images, this paper proposes a PMP method based on neighboring pixels merging, which meets the requirements of differential privacy. First, the gray scale matrix of the face image is leveled into a one-dimensional data stream and the position index of each pixel is recorded while traverse through the pixels. Given the large number of duplicate pixels in the face image, duplicate pixels as well as neighboring pixels were grouped to reduce the sensitivity of differential privacy, and noise was subsequently added to each group. Eventually, each pixel is replaced to its original position according to the index, and the new one-dimensional data stream is converted back to the original matrix structure. The experiment based on real face image data set used the combination of support vector machine (SVM) and principal component analysis (PCA). The results show that the PMP algorithm is 30.57% higher than the LAP algorithm in terms of the average accuracy of face recognition, and the proposed PMP algorithm has higher usability and robustness. © 2024 IEEE.

关键词： Differential privacy

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An Embedding-Based Multi-Modal Few-Shot Learning Method for Network Intrusion Detection 21

An Embedding-Based Multi-Modal Few-Shot Learning Method for ...

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21st International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Qiulin, He Jinquan, Zhang Yuanyuan, Huang School of Cybersecurity Xin Gu Industrial College Chengdu University of Information Technology Chengdu610225 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China Dazhou Key Laboratory Government Data Security Dazhou635000 China

ISBN: (纸本)9798331519254

In this paper, we introduce an embedding-based few-shot learning framework, aiming to achieve classification by calculating the similarity measure between unknown samples and known samples in the embedding space. This framework mainly consists of two parts: embedding space and similarity calculation stage. In the embedding space, semantic information and visual features are embedded into a common space to achieve multi-modal embedding. In the similarity calculation stage, the classification task is achieved by calculating multiple score indicators between vectors. The experimental results show that when the number of support sets is 5, the accuracy on the CICIDS2017 and KDDCUP99 datasets reaches 98.89% and 99.85%, demonstrating excellent performance in the domain of few-shot learning for intrusion detection. © 2024 IEEE.

关键词： Network intrusion

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Attacks Against Cross-Chain Systems and Defense Approaches:A Contemporary Survey

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1647-1667页

作者： Li Duan Yangyang Sun Wei Ni Weiping Ding Jiqiang Liu Wei Wang IEEE the Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong UniversityBeijing 100044China the Guangxi Key Laboratory of Cryptography and Information Security Guilin 541004China the Communications and Signal Processing Team Data61 Business UnitCommonwealth Scientific and Industrial Research Organisation(CSIRO)Sydney NSW 2109Australia the School of Information Science and Technology Nantong UniversityNantong 226019China

The blockchain cross-chain is a significant technology for inter-chain interconnection and value transfer among different blockchain ***-chain overcomes the“information island”problem of the closed blockchain network and is increasingly applied to multiple critical areas such as finance and the internet of things(IoT).Blockchain can be divided into three main categories of blockchain networks:public blockchains,private blockchains,and consortium ***,there are differences in block structures,consensus mechanisms,and complex working mechanisms among heterogeneous *** fragility of the cross-chain system itself makes the cross-chain system face some potential security and privacy *** paper discusses security defects on the cross-chain implementation mechanism,and discusses the impact of the structural features of blockchain networks on cross-chain *** terms of cross-chain intercommunication,a cross-chain attack can be divided into a multi-chain combination attack,native chain attack,and inter-chain attack *** various security threats and attack paths faced by the cross-chain system are *** last,the corresponding security defense methods of cross-chain security threats and future research directions for cross-chain applications are put forward.

关键词： Blockchain cross-chain defense distributed private key control hash-locking notary security threats sidechain/relay

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