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Manipulation Attacks in Local Differential Privacy [PDF]
The Journal of Privacy and Confidentiality, 2021 Local differential privacy is a widely studied restriction on distributed algorithms that collect aggregates about sensitive user data, and is now deployed in several large systems.
Albert Cheu, Adam Smith, Jonathan Ullman
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Local Differential Privacy for Evolving Data
The Journal of Privacy and Confidentiality, 2020 There are now several large scale deployments of differential privacy used to collect statistical information about users. However, these deployments periodically recollect the data and recompute the statistics using algorithms designed for a single use.
Matthew Joseph +3 more
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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.
Milan Lopuhaä-Zwakenberg +1 more
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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
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A Comprehensive Survey on Local Differential Privacy [PDF]
Security and Communication Networks, 2020 With the advent of the era of big data, privacy issues have been becoming a hot topic in public. Local differential privacy (LDP) is a state-of-the-art privacy preservation technique that allows to perform big data analysis (e.g., statistical estimation, statistical learning, and data mining) while guaranteeing each individual participant’s privacy. In
Xingxing Xiong +4 more
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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
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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
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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
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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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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
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