Results 21 to 30 of about 29,077 (283)
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
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.
Graham Cormode +5 more
openaire +2 more sources
Utility-optimized Local Differential Privacy Joint Distribution Estimation Mechanisms [PDF]
Jisuanji kexue, 2023 Compared with traditional centralized differential privacy,local differential privacy(LDP) has the advantage of not re-lying on trusted third parties,but it also has the problem of low data utility.The utility-optimized local differential privacy(ULDP ...
YIN Shiyu, ZHU Youwen, ZHANG Yue
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Privacy-Preserving Graph Embedding based on Local Differential Privacy
Proceedings of the 33rd ACM International Conference on Information and Knowledge ManagementGraph embedding has become a powerful tool for learning latent representations of nodes in a graph. Despite its superior performance in various graph-based machine learning tasks, serious privacy concerns arise when the graph data contains personal or sensitive information.
Zening Li +4 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
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
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Robust Optimization for Local Differential Privacy
2022 IEEE International Symposium on Information Theory (ISIT), 2022 We consider the setting of publishing data without leaking sensitive information. We do so in the framework of Robust Local Differential Privacy (RLDP). This ensures privacy for all distributions of the data in an uncertainty set. We formulate the problem of finding the optimal data release protocol as a robust optimization problem.
Jasper Goseling +1 more
openaire +4 more sources
Privacy Preservation in the Internet of Vehicles using Local Differential Privacy and IOTA Ledger
, 2023 With the growth in Vehicular Ad Hoc Network (VANET) technology, many vehicular devices are communicating with each other and with the edge nodes, generating a massive amount of data. One of the biggest challenges is to preserve users’ privacy as the data
Khan, A. +4 more
core +1 more source
, 2023 Advancements in Vehicular ad-hoc Network (VANET) technology have led to a growing network of interconnected devices, including edge devices, resulting in substantial data generation.
Khan, A. +5 more
core +1 more source
RCP:Mean Value Protection Technology Under Local Differential Privacy [PDF]
Jisuanji kexue, 2023 This paper mainly focuses on the mean estimation problem in differential privacy query.After introducing the current mainstream local differential privacy design scheme of numerical data mean estimation,it first introduces the random censoring mechanism ...
LIU Likang, ZHOU Chunlai
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