Results 31 to 40 of about 27,791 (249)
Multi-level local differential privacy algorithm recommendation framework
Tongxin xuebao, 2022 Local differential privacy (LDP) algorithm usually assigned the same protection mechanism and parameters to different users.However, it ignored the differences among the device resources and the privacy requirements of different users.For this reason, a ...
Hanyi WANG +5 more
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Context-Aware Local Differential Privacy
CoRR, 2019 Local differential privacy (LDP) is a strong notion of privacy for individual users that often comes at the expense of a significant drop in utility. The classical definition of LDP assumes that all elements in the data domain are equally sensitive. However, in many applications, some symbols are more sensitive than others. This work proposes a context-
Jayadev Acharya +4 more
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Local Differential Privacy for Deep Learning
IEEE Internet of Things Journal, 2020 The internet of things (IoT) is transforming major industries including but not limited to healthcare, agriculture, finance, energy, and transportation. IoT platforms are continually improving with innovations such as the amalgamation of software-defined networks (SDN) and network function virtualization (NFV) in the edge-cloud interplay. Deep learning
Mahawaga Arachchige Pathum Chamikara +5 more
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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
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Behavior Sequence Mining Model Based on Local Differential Privacy
IEEE Access, 2020 Most of local differential privacy frameworks target statistics on certain privacy behaviors of users, but not behavior sequence. In this paper, we explore and propose a behavior sequence mining model that satisfies the local differential privacy ...
Jianen Yan, Yan Wang, Wenling Li
doaj +1 more source
Privacy-Preserving Transactions with Verifiable Local Differential Privacy.
, 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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The Privacy-Utility Tradeoff of Robust Local Differential Privacy
CoRR, 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 $\operatorname{I}(X;Y)$, 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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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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Exponential Separations in Local Differential Privacy [PDF]
, 2020 We prove a general connection between the communication complexity of two-player games and the sample complexity of their multi-player locally private analogues. We use this connection to prove sample complexity lower bounds for locally differentially private protocols as straightforward corollaries of results from communication complexity.
Matthew Joseph +2 more
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Fldp: Flexible Strategy For Local Differential Privacy
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022 Local differential privacy (LDP), a technique applying unbiased statistical estimations instead of real data, is often adopted in data collection. In particular, this technique is used with frequency oracles (FO) because it can protect each user's privacy and prevent leakage of sensitive information.
Dan Zhao 0009 +5 more
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