Results 21 to 30 of about 882,964 (360)
Federated Learning With Differential Privacy: Algorithms and Performance Analysis [PDF]
IEEE Transactions on Information Forensics and Security, 2019 Federated learning (FL), as a type of distributed machine learning, is capable of significantly preserving clients’ private data from being exposed to adversaries.
Kang Wei +8 more
semanticscholar +1 more source
Rényi Differential Privacy [PDF]
IEEE Computer Security Foundations Symposium, 2017 We propose a natural relaxation of differential privacy based on the Rényi divergence. Closely related notions have appeared in several recent papers that analyzed composition of differentially private mechanisms. We argue that the useful analytical tool
Ilya Mironov
semanticscholar +1 more source
Digital technologies: tensions in privacy and data
Journal of the Academy of Marketing Science, 2022 Driven by data proliferation, digital technologies have transformed the marketing landscape. In parallel, significant privacy concerns have shaken consumer–firm relationships, prompting changes in both regulatory interventions and people’s own privacy ...
Sara Quach +4 more
semanticscholar +1 more source
Forensics and Anti-Forensics of a NAND Flash Memory: From a Copy-Back Program Perspective
IEEE Access, 2021 This paper proposes a safe copy-back program operation in a NAND flash memory, which is targeting digital forensics for a variety of reasons. Due to the background management operation of the NAND flash memory, the original data is highly likely to ...
Na Young Ahn, Dong Hoon Lee
doaj +1 more source
Adversarial attack and defense in reinforcement learning-from AI security view
Cybersecurity, 2019 Reinforcement learning is a core technology for modern artificial intelligence, and it has become a workhorse for AI applications ranging from Atrai Game to Connected and Automated Vehicle System (CAV).
Tong Chen +5 more
doaj +1 more source
Privacy Preserving Data Mining
Advances in Information Security, 2015 Recent interest in data collection and monitoring using data mining for security and business-related applications has raised privacy. Privacy Preserving Data Mining (PPDM) techniques require data modification to disinfect them from sensitive information
J. Vaidya, Yu Zhu, C. Clifton
semanticscholar +1 more source
Differential Privacy for Deep and Federated Learning: A Survey
IEEE Access, 2022 Users’ privacy is vulnerable at all stages of the deep learning process. Sensitive information of users may be disclosed during data collection, during training, or even after releasing the trained learning model.
Ahmed El Ouadrhiri, Ahmed M Abdelhadi
semanticscholar +1 more source
Privacy‐preserving federated learning based on multi‐key homomorphic encryption [PDF]
International Journal of Intelligent Systems, 2021 With the advance of machine learning and the Internet of Things (IoT), security and privacy have become critical concerns in mobile services and networks. Transferring data to a central unit violates the privacy of sensitive data.
Jing Ma, Si-Ahmed Naas, S. Sigg, X. Lyu
semanticscholar +1 more source
Numerical Evaluation of Job Finish Time Under MTD Environment
IEEE Access, 2020 Moving target defense (MTD) has recently emerged as a game-changer in the confrontation between cyberattack and defense. MTD mechanism constantly and randomly changes the system configurations to create uncertainty of the attack surface against cyber ...
Zhi Chen +3 more
doaj +1 more source
Dual Privacy-Preserving Lightweight Navigation System for Vehicular Networks
IEEE Access, 2022 As an indispensable part of intelligent transportation system, a traffic-sensitive navigation system can assist drivers in avoiding traffic congestion by providing navigation services.
Yingying Yao +4 more
doaj +1 more source