Results 31 to 40 of about 2,499,134 (251)
Certified Robustness to Adversarial Examples with Differential Privacy [PDF]
IEEE Symposium on Security and Privacy, 2018 Adversarial examples that fool machine learning models, particularly deep neural networks, have been a topic of intense research interest, with attacks and defenses being developed in a tight back-and-forth.
Mathias Lécuyer +4 more
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Towards Private Learning on Decentralized Graphs With Local Differential Privacy [PDF]
IEEE Transactions on Information Forensics and Security, 2022 Many real-world networks are inherently decentralized. For example, in social networks, each user maintains a local view of a social graph, such as a list of friends and her profile. It is typical to collect these local views of social graphs and conduct
Wanyu Lin, Baochun Li, Cong Wang
semanticscholar +1 more source
Fully Adaptive Composition in Differential Privacy [PDF]
International Conference on Machine Learning, 2022 Composition is a key feature of differential privacy. Well-known advanced composition theorems allow one to query a private database quadratically more times than basic privacy composition would permit.
J. Whitehouse +3 more
semanticscholar +1 more source
Privacy-Preserving Monotonicity of Differential Privacy Mechanisms
Applied Sciences, 2018 Differential privacy mechanisms can offer a trade-off between privacy and utility by using privacy metrics and utility metrics. The trade-off of differential privacy shows that one thing increases and another decreases in terms of privacy metrics and ...
Hai Liu +5 more
doaj +1 more source
Tempered Sigmoid Activations for Deep Learning with Differential Privacy [PDF]
AAAI Conference on Artificial Intelligence, 2020 Because learning sometimes involves sensitive data, machine learning algorithms have been extended to offer differential privacy for training data. In practice, this has been mostly an afterthought, with privacy-preserving models obtained by re-running ...
Nicolas Papernot +4 more
semanticscholar +1 more source
Heterogeneous Differential Privacy
The Journal of Privacy and Confidentiality, 2017 The massive collection of personal data by personalization systems has rendered the preservation of privacy of individuals more and more difficult. Most of the proposed approaches to preserve privacy in personalization systems usually address this issue ...
Mohammad Alaggan +2 more
doaj +1 more source
Safe consensus control of cooperative-competitive multi-agent systems via differential privacy
Kybernetika (Praha), 2022 This paper investigates a safe consensus problem for cooperative-competitive multi-agent systems using a differential privacy (DP) approach. Considering that the agents simultaneously interact cooperatively and competitively, we propose a novel DP ...
Jiayue Ma, Jiangping Hu
semanticscholar +1 more source
The Journal of Privacy and Confidentiality, 2013 We propose a relaxed privacy definition called {\em random differential privacy} (RDP). Differential privacy requires that adding any new observation to a database will have small effect on the output of the data-release procedure.
Robert Hall +2 more
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Federated learning and differential privacy for medical image analysis
Scientific Reports, 2021 The artificial intelligence revolution has been spurred forward by the availability of large-scale datasets. In contrast, the paucity of large-scale medical datasets hinders the application of machine learning in healthcare.
Mohammed Adnan +4 more
semanticscholar +1 more source
Combinational Randomized Response Mechanism for Unbalanced Multivariate Nominal Attributes
IEEE Access, 2020 At present, many enterprises provide users with better services by collecting their sensitive information. However, these enterprises will inevitably cause the leakage of users' information, thereby infringing on users' privacy.
Xuejie Feng +3 more
doaj +1 more source