Results 41 to 50 of about 29,077 (283)
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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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
doaj +2 more sources
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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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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Local Differential Privacy for Bayesian Optimization
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 Motivated by the increasing concern about privacy in nowadays data-intensive online learning systems, we consider a black-box optimization in the nonparametric Gaussian process setting with local differential privacy (LDP) guarantee. Specifically, the rewards from each user are further corrupted to protect privacy and the learner only has access to the
Xingyu Zhou 0001, Jian Tan
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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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, 2023 Matrix factorization (MF) is a prevailing technique in recommendation systems (RSs). Since MF needs to process a large amount of user data when generating recommendation results, privacy protection is increasingly being valued by users.
Ran, Xun +4 more
core +1 more source
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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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
Secure hot path crowdsourcing with local differential privacy under fog computing architecture
, 2020 Crowdsourcing plays an essential role in the Internet of Things (IoT) for data collection, where a group of workers is equipped with Internet-connected geolocated devices to collect sensor data for marketing or research purpose.
Tjuawinata, Ivan +4 more
core +1 more source