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Threats,attacks and defenses to federated learning:issues,taxonomy and perspectives

Cybersecurity 2022年第2期5卷 56-74页

作者： Pengrui Liu Xiangrui Xu Wei Wang Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong UniversityBeijing100044China

Empirical attacks on Federated Learning(FL)systems indicate that FL is fraught with numerous attack surfaces throughout the FL *** attacks can not only cause models to fail in specific tasks,but also infer private *** previous surveys have identified the risks,listed the attack methods available in the literature or provided a basic taxonomy to classify them,they mainly focused on the risks in the training phase of *** this work,we survey the threats,attacks and defenses to FL throughout the whole process of FL in three phases,including Data and Behavior Auditing Phase,Training Phase and Predicting *** further provide a comprehensive analysis of these threats,attacks and defenses,and summarize their issues and *** work considers security and privacy of FL based on the viewpoint of the execution process of *** highlight that establishing a trusted FL requires adequate measures to mitigate security and privacy threats at each ***,we discuss the limitations of current attacks and defense approaches and provide an outlook on promising future research directions in FL.

关键词： Federated learning Security and privacy threats Multi-phases Inference attacks Poisoning attacks evasion attacks Defenses Trusted

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Cryptographic Based Secure Model on Dataset for Deep Learning Algorithms

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Computers, Materials & Continua 2021年第10期69卷 1183-1200页

作者： Muhammad Tayyab Mohsen Marjani N.Z.Jhanjhi Ibrahim Abaker Targio Hashim Abdulwahab Ali Almazroi Abdulaleem Ali Almazroi School of Computer Science and Engineering(SCE) Taylor’s University Lake-Side CampusSubang Jaya47500Malaysia Department of Computer Science College of Computing and InformaticsUniversity of SharjahSharjah27272UAE University of Jeddah College of Computing and Information Technology at KhulaisDepartment of Information TechnologyJeddahSaudi Arabia Department of Computer Science Rafha Community CollegeNorthern Border UniversityArar91431Saudi Arabia

Deep learning(DL)algorithms have been widely used in various security applications to enhance the performances of decision-based *** data added by an attacker can cause several security and privacy problems in the operation of DL *** two most common active attacks are poisoning and evasion attacks,which can cause various problems,including wrong prediction and misclassification of decision-based ***,to design an efficient DL model,it is crucial to mitigate these *** this regard,this study proposes a secure neural network(NN)model that provides data security during model training and testing *** main idea is to use cryptographic functions,such as hash function(SHA512)and homomorphic encryption(HE)scheme,to provide authenticity,integrity,and confidentiality of *** performance of the proposed model is evaluated by experiments based on accuracy,precision,attack detection rate(ADR),and computational *** results show that the proposed model has achieved an accuracy of 98%,a precision of 0.97,and an ADR of 98%,even for a large number of ***,the proposed model can be used to detect attacks and mitigate the attacker *** results also show that the computational cost of the proposed model does not increase with model complexity.

关键词： Deep learning(DL) poisoning attacks evasion attacks neural network hash functions SHA512 homomorphic encryption scheme

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