Context-aware Data Aggregation with Localized Information Privacy

Jiang, Bo; Li, Ming; Tandon, Ravi

doi:10.1109/cns.2018.8433200

Cited by 17 publications

(12 citation statements)

References 23 publications

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“…Some studies have presented several related definitions of LDP. Jiang et al [16] proposed a novel definition of Localized Information Privacy (LIP) for frequency estimation of context-aware data, which relaxes the classic LDP by introducing a context-aware knowledge (priors) to increase statistics utility. LIP is identical to ε-adversarial privacy (ε-AP) [17] and implies ε-Mutual Information Privacy (ε-MIP) [18].…”

Section: Definition 1 (Local Differential Privacymentioning

confidence: 99%

A Comprehensive Survey on Local Differential Privacy

Xiong

Liu

et al. 2020

Security and Communication Networks

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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 this paper, we present a comprehensive survey of LDP. We first give an overview on the fundamental knowledge of LDP and its frameworks. We then introduce the mainstream privatization mechanisms and methods in detail from the perspective of frequency oracle and give insights into recent studied on private basic statistical estimation (e.g., frequency estimation and mean estimation) and complex statistical estimation (e.g., multivariate distribution estimation and private estimation over complex data) under LDP. Furthermore, we present current research circumstances on LDP including the private statistical learning/inferencing, private statistical data analysis, privacy amplification techniques for LDP, and some application fields under LDP. Finally, we identify future research directions and open challenges for LDP. This survey can serve as a good reference source for the research of LDP to deal with various privacy-related scenarios to be encountered in practice.

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Section: Definition 1 (Local Differential Privacymentioning

confidence: 99%

A Comprehensive Survey on Local Differential Privacy

Xiong

Liu

et al. 2020

Security and Communication Networks

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“…When adversary possesses knowledge of a priori probability distribution of input, LDP can lead to the risk of privacy leakage [ 2 , 17 , 18 , 19 , 20 , 21 , 22 ]. However, a better privacy-utility tradeoff can be achieved by incorporating the attacker’s knowledge into the LDP.…”

Section: Privacy Threat Model On Information-theoretic Channelmentioning

confidence: 99%

GDP vs. LDP: A Survey from the Perspective of Information-Theoretic Channel

Liu

Peng

Tian

et al. 2022

Entropy

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The existing work has conducted in-depth research and analysis on global differential privacy (GDP) and local differential privacy (LDP) based on information theory. However, the data privacy preserving community does not systematically review and analyze GDP and LDP based on the information-theoretic channel model. To this end, we systematically reviewed GDP and LDP from the perspective of the information-theoretic channel in this survey. First, we presented the privacy threat model under information-theoretic channel. Second, we described and compared the information-theoretic channel models of GDP and LDP. Third, we summarized and analyzed definitions, privacy-utility metrics, properties, and mechanisms of GDP and LDP under their channel models. Finally, we discussed the open problems of GDP and LDP based on different types of information-theoretic channel models according to the above systematic review. Our main contribution provides a systematic survey of channel models, definitions, privacy-utility metrics, properties, and mechanisms for GDP and LDP from the perspective of information-theoretic channel and surveys the differential privacy synthetic data generation application using generative adversarial network and federated learning, respectively. Our work is helpful for systematically understanding the privacy threat model, definitions, privacy-utility metrics, properties, and mechanisms of GDP and LDP from the perspective of information-theoretic channel and promotes in-depth research and analysis of GDP and LDP based on different types of information-theoretic channel models.

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“…In multiparty DP [167], the view of each subgroup of players is differentially private with respect to other players. Some variants introduced in this work were also considered in the local setting; examples include localized information privacy [91], one-sided local DP [130], personalized local DP [132], and d D -local DP [4,81] (called condensed local DP in [81]).…”

Section: Local Modelmentioning

confidence: 99%

SoK: Differential privacies

Desfontaines

Pejó

2020

Proceedings on Privacy Enhancing Technologies

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Shortly after it was first introduced in 2006, differential privacy became the flagship data privacy definition. Since then, numerous variants and extensions were proposed to adapt it to different scenarios and attacker models. In this work, we propose a systematic taxonomy of these variants and extensions. We list all data privacy definitions based on differential privacy, and partition them into seven categories, depending on which aspect of the original definition is modified.These categories act like dimensions: variants from the same category cannot be combined, but variants from different categories can be combined to form new definitions. We also establish a partial ordering of relative strength between these notions by summarizing existing results. Furthermore, we list which of these definitions satisfy some desirable properties, like composition, postprocessing, and convexity by either providing a novel proof or collecting existing ones.

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Context-aware Data Aggregation with Localized Information Privacy

Cited by 17 publications

References 23 publications

A Comprehensive Survey on Local Differential Privacy

A Comprehensive Survey on Local Differential Privacy

GDP vs. LDP: A Survey from the Perspective of Information-Theoretic Channel

SoK: Differential privacies

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