Results 11 to 20 of about 166,795 (258)
Safeguarding cross-silo federated learning with local differential privacy
Digital Communications and Networks, 2022 Federated Learning (FL) is a new computing paradigm in privacy-preserving Machine Learning (ML), where the ML model is trained in a decentralized manner by the clients, preventing the server from directly accessing privacy-sensitive data from the clients.
Chen Wang +5 more
doaj +3 more sources
Privacy Enhanced Matrix Factorization for Recommendation with Local Differential Privacy
IEEE Transactions on Knowledge and Data Engineering, 2018 Recommender systems are collecting and analyzing user data to provide better user experience. However, several privacy concerns have been raised when a recommender knows user's set of items or their ratings. A number of solutions have been suggested to improve privacy of legacy recommender systems, but the existing solutions in the literature can ...
Hyejin Shin +3 more
openaire +5 more sources
Robust Local Differential Privacy
2021 IEEE International Symposium on Information Theory (ISIT), 2021 We consider data release protocols for data X = (S, U), where S is sensitive; the released data Y contains as much information about X as possible, measured as I(X; Y ), without leaking too much about S. We introduce the Robust Local Differential Privacy (RLDP) framework to measure privacy.
Lopuhaä-Zwakenberg, Milan +1 more
openaire +2 more sources
Local Differential Privacy for Person-to-Person Interactions
IEEE Open Journal of the Computer Society, 2022 Currently, many global organizations collect personal data for marketing, recommendation system improvement, and other purposes. Some organizations collect personal data securely based on a technique known as $\epsilon$-local differential privacy (LDP ...
Yuichi Sei, Akihiko Ohsuga
doaj +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
Private rank aggregation under local differential privacy [PDF]
International Journal of Intelligent Systems, 2020 As a method for answer aggregation in crowdsourced data management, rank aggregation aims to combine different agents' answers or preferences over the given alternatives into an aggregate ranking which agrees the most with the preferences. However, since the aggregation procedure relies on a data curator, the privacy within the agents' preference data ...
Yan, Ziqi, Li, Gang, Liu, Jiqiang
openaire +2 more sources
New Program Abstractions for Privacy [PDF]
, 2020 Static program analysis, once seen primarily as a tool for optimising programs, is now increasingly important as a means to provide quality guarantees about programs. One measure of quality is the extent to which programs respect the privacy of user data.
C Dwork +5 more
core +1 more source
Proceedings of the 2018 International Conference on Management of Data, 2018 Local differential privacy (LDP), where users randomly perturb their inputs to provide plausible deniability of their data without the need for a trusted party, has been adopted recently by several major technology organizations, including Google, Apple and Microsoft.
Cormode, Graham +5 more
openaire +2 more sources
Review of Differential Privacy Research [PDF]
Jisuanji kexue, 2023 In the past decade,widespread data collection has become the norm.With the rapid development of large-scale data analysis and machine learning,data privacy is facing fundamental challenges.Exploring the trade-offs between privacy protection and data ...
ZHAO Yuqi, YANG Min
doaj +1 more source
Local Differential Privacy for Federated Learning
, 2022 17 ...
Pathum Chamikara Mahawaga Arachchige +6 more
openaire +2 more sources