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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Hu, Jiacong Chen, Hao Chen, Kejia Gao, Yang Ye, Jingwen Wang, Xingen Song, Mingli Feng, Zunlei College of Computer Science and Technology Zhejiang University China School of Software Technology Zhejiang University China National University of Singapore Singapore State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China Bangsheng Technology Co. Ltd. China

Due to its powerful representational capabilities, Transformers have gradually become the mainstream model in the field of machine vision. However, the vast and complex parameters of Transformers impede researchers from gaining a deep understanding of their internal mechanisms, especially error mechanisms. Existing methods for interpreting Transformers mainly focus on understanding them from the perspectives of the importance of input tokens or internal modules, as well as the formation and meaning of features. In contrast, inspired by research on information integration mechanisms and conjunctive errors in the biological visual system, this paper conducts an in-depth exploration of the internal error mechanisms of Transformers. We first propose an information integration hypothesis for Transformers in the machine vision domain and provide substantial experimental evidence to support this hypothesis. This includes the dynamic integration of information among tokens and the static integration of information within tokens in Transformers, as well as the presence of conjunctive errors therein. Addressing these errors, we further propose heuristic dynamic integration constraint methods and rule-based static integration constraint methods to rectify errors and ultimately improve model performance. The entire methodology framework is termed as Transformer Doctor, designed for diagnosing and treating internal errors within transformers. Through a plethora of quantitative and qualitative experiments, it has been demonstrated that Transformer Doctor can effectively address internal errors in transformers, thereby enhancing model performance. For more information, please visit https://***/. © 2024 Neural information processing systems foundation. All rights reserved.

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An Effective security Comparison Protocol in Cloud Computing

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Computers, Materials & Continua 2023年第6期75卷 5141-5158页

作者： Yuling Chen Junhong Tao Tao Li Jiangyuan Cai Xiaojun Ren State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550000China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic TechnologyGuilin550000China POWERCHINA Guizhou Electric Power Engineering Co. Ltd.Guiyang550000China Blockchain Laboratory of Agricultural Vegetables Weifang University of Science and TechnologyWeifang261000China

With the development of cloud computing technology,more and more data owners upload their local data to the public cloud server for storage and *** this can save customers’operating costs,it also poses privacy and security *** challenges can be solved using secure multi-party computation(SMPC),but this still exposes more security *** cloud computing using SMPC,clients need to process their data and submit the processed data to the cloud server,which then performs the calculation and returns the results to each *** client and server must be *** there is cooperation or dishonest behavior between clients,some clients may profit from it or even disclose the private data of other *** paper proposes the SMPC based on a Partially-Homomorphic Encryption(PHE)scheme in which an addition homomorphic encryption algorithm with a lower computational cost is used to ensure data comparability and Zero-Knowledge Proof(ZKP)is used to limit the client’s malicious *** addition,the introduction of Oblivious Transfer(OT)technology also ensures that the semi-honest cloud server knows nothing about private data,so that the cloud server of this scheme can calculate the correct data in the case of malicious participant models and safely return the calculation results to each ***,the security analysis shows that the scheme not only ensures the privacy of participants,but also ensures the fairness of the comparison protocol data.

关键词： Secure comparison protocols zero-knowledge proof homomorphic encryption cloud computing

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A security Transaction Model Based on HTLC 15

A Security Transaction Model Based on HTLC

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2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green Computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th IEEE International Conference on Smart data, Smartdata 2022

作者： He, Xiaochuan Tan, Chaoyue Wu, Yongtang Chen, Yuling Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology College of Computer Science and Technology Guiyang China Guilin University of Electronic Technology Guangxi Key Laboratory of Cryptography and Information Security Guilin China Weifang University of Science and Technology Blockchain Laboratory of Agricultural Vegetables Weifang Key Laboratory of Blockchain on Agricultural Vegetables Weifang China Weifang Key Laboratory of Blockchain on Agricultural Vegetables Weifang University of Science and Technology Weifang China

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

Hashed Timelock Contract (HTLC), as one of the important parts of Lightning Network (LN), provides technical support for cross-node transactions. However, security and privacy issues, such as preimage transmiting and unbalanced funds, should be solved to improve the robustness of LN. In this work, we first design a protocol to transmit preimage based on DH key exchange algorithm. And then, we design a funds balance mechanism to prevent the LN nodes monopolize market while it accumulating enough funds. Fianlly, experiment shows that the proposed model is more efficient than traditional HTLC model and it can balance the allocation of funds in LN. © 2022 IEEE.

关键词： Analytical models Social computing Privacy Protocols Computational modeling Robustness security

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A blockchain-based Scalable Electronic Contract Signing System 15

A Blockchain-based Scalable Electronic Contract Signing Syst...

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作者： Yang, Kaicheng Wu, Yongtang Chen, Yuling State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guangxi China Blockchain Laboratory of Agricultural Vegetables Weifang University of Science and Technology Weifang Key Laboratory of Blockchain on Agricultural Vegetables WeiFang China Blockchain Laboratory of Agricultural Vegetables Weifang University of Science and Technology WeiFang China

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

As the COVID-19 continues to spread globally, more and more companies are transforming into remote online offices, leading to the expansion of electronic signatures. However, the existing electronic signatures platform has the problem of data-centered management. The system is subject to data loss, tampering, and leakage when an attack from outside or inside occurs. In response to the above problems, this paper designs an electronic signature solution and implements a prototype system based on the consortium blockchain. The solution divides the contract signing process into four states: contract upload, initiation signing, verification signing, and confirm signing. The signing process is mapped with the blockchain-linked data. Users initiate the signature transaction by signing the uploaded contract's hash. The sign state transition is triggered when the transaction is uploaded to the blockchain under the consensus mechanism and the smart contract control, which effectively ensures the integrity of the electronic contract and the non-repudiation of the electronic signature. Finally, the blockchain performance test shows that the system can be applied to the business scenario of contract signing. © 2022 IEEE.

关键词： Smart contract

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A K-anonymity Optimization Algorithm Under Attack Model 15

A K-anonymity Optimization Algorithm Under Attack Model

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作者： Yang, Manxiang Wu, Yongtang Chen, Yuling State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guangxi China Blockchain Laboratory of Agricultural Vegetables Weifang University of Science and Technology Weifang Key Laboratory of Blockchain on Agricultural Vegetables WeiFang China

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

The current k-anonymity algorithm still suffers from problems such as location information leakage and poor quality of service due to improper selection of k. To tackle the above drawbacks, we propose a personalized k-anonymity optimization algorithm under attack model. Achieving optimal quality of service and personalized privacy protection in a context where attackers launch the strongest attacks based on a priori knowledge. Quantifying location privacy and quality of service based on location entropy and attack models. On this basis, the optimal k is obtained through the game to achieve privacy and service quality optimization. security analysis and simulation experiments demonstrate the privacy and effectiveness of the algorithm. The experimental results show the specific k for different scenarios and different anonymity mechanisms. © 2022 IEEE.

关键词： Game theory

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Covert Timing Channel Detection Based on Isolated Binary Trees

SSRN

引用

SSRN 2024年

作者： Lin, Yuwei Chen, Yonghong Zhuang, Xiaolong Tian, Hui College of Computer Secience and Techinology Huaqiao University Xiamen China Xiamen Key Laboratory of Data Security and Blockchain Technology Xiamen China

Network covert timing channel threats to network security by altering the timing characteristics ofnetwork packets to transmit confidential information. In this paper, we propose a detection methodbased on isolated binary trees, aiming to address the problem of the novel covert channel imitatinglegitimate traffic patterns and injecting additional anomalies to evade detection. This method is basedon the Isolation Forest algorithm, which can be classified into different categories by analyzing thestepwise function features of network traffic and using isolation binary trees generated with randomsplit thresholds. At the same time, we validate the proposed detection model using a publicly availabledataset. The experimental results demonstrate that eliminating outliers significantly enhances thestepwise function features while preserving the original form of legitimate traffic. Compared to themodel without outlier handling, the average AUC scores for TRCTC and Jitterbug improved by 7.37%and 2.23%, respectively. Furthermore, we achieved superior performance on a new channel named Ε-κlibur and Ε-κlibur-O compared to using deep learning-based detection methods. © 2024, The Authors. All rights reserved.

关键词： Binary trees

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A Secure Comparison Protocol in the Malicious Model 15

A Secure Comparison Protocol in the Malicious Model

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作者： Tao, Junhong Wu, Yongtang Chen, Yuling Guizhou University State Key Laboratory of Public Big Data College of Computer Science and and Technology Guiyang China Guilin University of Electronic Technology Guangxi Key Laboratory of Cryptography and Information Security Guilin China Weifang University of Science and Technology Blockchain Laboratory of Agricultural Vegetables WeiFang China Weifang Key Laboratory of Blockchain on Agricultural Vegetables WeiFang China

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

Secure comparison is an important problem in the field of secure computing. A classic example is the Millionaires' problem, which is securely compares the wealth of two millionaires without revealing their wealth. However most traditional secure comparison protocol requires autonomous computation among the participants. It is necessary to ensure that the model of the participants is a semi-honest model, otherwise the protocol will not be able to execute correctly. In this paper, we introduce a semi-honest judge to improve the Damgård-Geisler-KrOigaard(DGK) comparison protocol by using techniques such as zero-knowledge proof and homomorphic encryption. The improved protocol is not only applicable to the malicious participant model, but also the introduced judge would not get any information of data. At last, the security analysis shows that the proposed scheme in this paper can guarantee the data fairness of the comparison protocol while ensuring the privacy security of the participants. © 2022 IEEE.

关键词： Cryptography

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Rotation-DPeak: Improving Density Peaks Selection for Imbalanced data 1

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8th China Computer Federation International Conference on Big data, CCF BIGdata 2020

作者： Hu, Xiaoliang Yan, Ming Chen, Yewang Yang, Lijie Du, Jixiang The College of Computer Science and Technology Huaqiao University Xiamen China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Soochow China Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Xiamen China

ISBN: (数字)9789811607059

ISBN: (纸本)9789811607042

Density Peak (DPeak) is an effective clustering algorithm. It maps arbitrary dimensional data onto a 2-dimensional space, which yields cluster centers and outliers automatically distribute on upper right and upper left corner, respectively. However, DPeak is not suitable for imbalanced data set with large difference in density, where sparse clusters are usually not identified. Hence, an improved DPeak, namely Rotation-DPeak, is proposed to overcome this drawback according to an simple idea: the higher density of a point p, the larger δ it should have such that p can be picked as a density peak, where δ is the distance from p to its nearest neighbor with higher density. Then, we use a quadratic curve to select points with the largest decision gap as density peaks, instead of choosing points with the largest γ, where γ= ρ× δ. Experiments shows that the proposed algorithm obtains better performance on imbalanced data set, which proves that it is promising. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Clustering algorithms

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LAPAA: A Secure and Lightweight Authentication Protocol for Edge Computing Built on ASCON Algorithm

LAPAA: A Secure and Lightweight Authentication Protocol for ...

引用

Next Generation data-driven Networks (NGDN), International Conference on

作者： Linghui Zhang Changli Zhou Jingliang Wen College of Computer Science and Technology HuaQiao University Xiamen China Xiamen Key Laboratory of Data Security and Blockchain Technology HuaQiao University Xiamen China

ISBN: (数字)9798350388374

ISBN: (纸本)9798350388381

Facing the challenges of next-generation network security, Mobile Edge Computing (MEC) serves as a decentralized model designed to meet the growing demands for data processing and reduce latency, making its security increasingly critical. However, with the rapid development of edge computing technology, traditional authentication protocols have become inadequate due to their complexity, insufficient protection of user anonymity, and neglect of resource constraints. These issues are at the core of current network security challenges. In response, this paper innovatively proposes LAPAA—a secure and lightweight authentication protocol for Edge computing built on ASCON algorithm. LAPAA effectively addresses the problems found in previous solutions and protects user privacy through anonymity by combining the symmetric encryption algorithm ASCON with asymmetric elliptic curve cryptography (ECC). It employs clever parameter and computation strategies to maintain key consistency between edge devices and MEC servers, while avoiding the leakage of users' real identities. Furthermore, due to the lightweight nature of the ASCON algorithm itself, LAPAA provides superior low-latency performance for the protocol. Additionally, through simulation verification and informal security analysis using the Scyther tool, LAPAA demonstrates its strong defense against various malicious threats and comparative evaluations with other protocols highlight LAPAA’s significant advantages in various aspects. Collectively, this research offers a secure and streamlined authentication strategy for MEC contexts, underscoring LAPAA’s inventive and groundbreaking approach to overcoming prevalent obstacles.

关键词： Privacy Protocols Multi-access edge computing Authentication Network security Elliptic curve cryptography Reliability engineering Robustness Servers Next generation networking

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Differentially Private and Heterogeneity-Robust Federated Learning With Theoretical Guarantee

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6369-6384页

作者： Wang, Xiuhua Wang, Shuai Li, Yiwei Fan, Fengrui Li, Shikang Lin, Xiaodong Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen361024 China University of Guelph School of Computer Science GuelphONN1G 2W1 Canada

Federated learning (FL) is a popular distributed paradigm where enormous clients collaboratively train a machine learning (ML) model under the orchestration of a central server without knowing the clients' private raw data. The development of effective FL algorithms faces multiple practical challenges including data heterogeneity and clients' privacy protection. Despite that numerous attempts have been made to deal with data heterogeneity or rigorous privacy protection, none have effectively tackled both issues simultaneously. In this article, we propose a differentially private and heterogeneity-robust FL algorithm, named DP-FedCVR to mitigate the data heterogeneity by following the client-variance-reduction strategy. Besides, it adopts a sophisticated differential privacy (DP) mechanism where the privacy-amplified strategy is applied, to achieve a rigorous privacy protection guarantee. We show that the proposed DP-FedCVR algorithm maintains its heterogeneity-robustness though DP noises are incorporated, while achieving a sublinear convergence rate for a nonconvex FL problem. Numerical experiments based on image classification tasks are presented to demonstrate that DP-FedCVR provides superior performance over the benchmark algorithms in the presence of data heterogeneity and various DP privacy budgets. © 2020 IEEE.

关键词： Federated learning

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