Results 11 to 20 of about 29,077 (283)
Robust Local Differential Privacy [PDF]
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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Privacy-Preserving Transactions with Verifiable Local Differential Privacy. [PDF]
, 2023 Privacy-preserving transaction systems on blockchain networks like Monero or Zcash provide complete transaction anonymity through cryptographic commitments or encryption. While this secures privacy, it inhibits the collection of statistical data, which current financial markets heavily rely on for economic and sociological research conducted by central
Danielle Movsowitz-Davidow +2 more
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Local Differential Privacy for Belief Functions
Proceedings of the AAAI Conference on Artificial Intelligence, 2022 In this paper, we propose two new definitions of local differential privacy for belief functions. One is based on Shafer’s semantics of randomly coded messages and the other from the perspective of imprecise probabilities. We show that such basic properties as composition and post-processing also hold for our new definitions.
Qiyu Li +3 more
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Extremal Mechanisms for Local Differential Privacy
J. Mach. Learn. Res., 2014 Local differential privacy has recently surfaced as a strong measure of privacy in contexts where personal information remains private even from data analysts. Working in a setting where both the data providers and data analysts want to maximize the utility of statistical analyses performed on the released data, we study the fundamental trade-off ...
Peter Kairouz +2 more
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Privacy-Preserving IDS for In-Vehicle Networks with Local Differential Privacy
, 2021 Intrusion Detection Systems (IDS) for In-Vehicle Networks routinely collect and transfer data about attacks to remote servers. However, the analysis of such data enables the inference of sensitive details about the driver’s identity and daily routine, violating privacy expectations.
Peter Franke +2 more
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Edge Local Differential Privacy for Dynamic Graphs
, 2023 AbstractHuge amounts of data are generated and shared in social networks and other network topologies. This raises privacy concerns when such data is not protected from leaking sensitive or personal information. Network topologies are commonly modeled through static graphs.
Sudipta Paul 0008 +2 more
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On robustness and local differential privacy [PDF]
The Annals of Statistics, 2023 It is of soaring demand to develop statistical analysis tools that are robust against contamination as well as preserving individual data owners' privacy. In spite of the fact that both topics host a rich body of literature, to the best of our knowledge, we are the first to systematically study the connections between the optimality under Huber's ...
Li, Mengchu, Berrett, Thomas B., Yu, Yi
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Research on federated learning approach based on local differential privacy [PDF]
Tongxin xuebao, 2022 As a type of collaborative machine learning framework, federated learning is capable of preserving private data from participants while training the data into useful models.Nevertheless, from a viewpoint of information theory, it is still vulnerable for ...
Haiyan KANG, Yuanrui JI
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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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