Results 11 to 20 of about 7,877,596 (288)
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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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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Quantum Differential Privacy in the Local Model
IEEE Transactions on Information TheoryDifferential privacy provides a robust framework for protecting sensitive data, while maintaining its utility for computation. In essence, a differentially private algorithm takes as input the data of multiple parties, and returns an output disclosing minimal information about any individual party.
Armando Angrisani, Elham Kashefi
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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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Local Differential Privacy for Data Clustering
Proceedings of the 21st International Conference on Security and CryptographyThis study presents an innovative framework that utilizes Local Differential Privacy (LDP) to address the challenge of data privacy in practical applications of data clustering. Our framework is designed to prioritize the protection of individual data privacy by empowering users to proactively safeguard their information before it is shared to any ...
Bruder, Lisa, Alishahi, Mina
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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
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
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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
doaj +1 more source
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 +6 more sources