Results 11 to 20 of about 164,354 (261)
Extremal Mechanisms for Local Differential Privacy [PDF]
, 2015 Local differential privacy has recently surfaced as a strong measure of privacy in contexts where personal information remains private even from data analysts.
Kairouz, Peter +2 more
core +2 more sources
Fisher Information Under Local Differential Privacy [PDF]
IEEE Journal on Selected Areas in Information Theory, 2020 We develop data processing inequalities that describe how Fisher information from statistical samples can scale with the privacy parameter $\varepsilon$ under local differential privacy constraints. These bounds are valid under general conditions on the distribution of the score of the statistical model, and they elucidate under which conditions the ...
Leighton Pate Barnes +2 more
openaire +2 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
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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
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
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
Local Differential Privacy for Federated Learning
, 2022 17 ...
Pathum Chamikara Mahawaga Arachchige +6 more
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Real-world trajectory sharing with local differential privacy [PDF]
Proceedings of the VLDB Endowment, 2021 Sharing trajectories is beneficial for many real-world applications, such as managing disease spread through contact tracing and tailoring public services to a population's travel patterns. However, public concern over privacy and data protection has limited the extent to which this data is shared.
Cunningham, Teddy +3 more
openaire +3 more sources
Local differential privacy for human-centered computing
EURASIP Journal on Wireless Communications and Networking, 2020 Human-centered computing in cloud, edge, and fog is one of the most concerning issues. Edge and fog nodes generate huge amounts of data continuously, and the analysis of these data provides valuable information. But they also increase privacy risks.
Xianjin Fang, Qingkui Zeng, Gaoming Yang
doaj +1 more source