Local Differential Privacy and Its Applications: A Comprehensive Survey

Yang, Mengmeng; Lyu, Lingjuan; Zhao, Jun; Zhu, Tianqing; Lam, Kwok‐Yan

doi:10.48550/arxiv.2008.03686

Cited by 35 publications

(31 citation statements)

References 132 publications

(146 reference statements)

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“…Frequency estimation with local differential privacy. Frequency estimation mechanisms which provide local differential privacy against the server have been extensively studied in the literature [24]. One of the simplest methods to achieve this is the randomized response.…”

Section: Related Workmentioning

confidence: 99%

Fair and Differentially Private Distributed Frequency Estimation

Yang,

Tjuawinata,

Lam

et al. 2021

Preprint

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In order to remain competitive, Internet companies collect and analyse user data for the purpose of improving user experiences. Frequency estimation is a widely used statistical tool which could potentially conflict with the relevant privacy regulations. Privacy preserving analytic methods based on differential privacy have been proposed, which either require a large user base or a trusted server; hence may give big companies an unfair advantage while handicapping smaller organizations in their growth opportunity. To address this issue, this paper proposes a fair privacy-preserving sampling-based frequency estimation method and provides a relation between its privacy guarantee, output accuracy, and number of participants. We designed decentralized privacy-preserving aggregation mechanisms using multi-party computation technique and established that, for a limited number of participants and a fixed privacy level, our mechanisms perform better than those that are based on traditional perturbation methods; hence, provide smaller companies a fair growth opportunity. We further propose an architectural model to support weighted aggregation in order to achieve higher accuracy estimate to cater for users with different privacy requirements. Compared to the unweighted aggregation, our method provides a more accurate estimate. Extensive experiments are conducted to show the effectiveness of the proposed methods.

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Section: Related Workmentioning

confidence: 99%

Fair and Differentially Private Distributed Frequency Estimation

Yang,

Tjuawinata,

Lam

et al. 2021

Preprint

Self Cite

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“…In the centralized model, the data are sent to a trusted entity that applies DP algorithms and then shares the anonymized dataset with an untrusted third-party client [19]. On the contrary, the local model assumes all external entities and communication channels as untrusted [20], [21]. In such a situation, local DP techniques aim at performing the data perturbation locally before releasing any dataset to an external party.…”

Section: Anonymization Techniquesmentioning

confidence: 99%

You can't always get what you want: towards user-controlled privacy on Android

Caputo,

Pagano,

Bottino

et al. 2021

Preprint

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Mobile applications (hereafter, apps) collect a plethora of information regarding the user behavior and his device through third-party analytics libraries. However, the collection and usage of such data raised several privacy concerns, mainly because the end-user -i.e., the actual owner of the data -is out of the loop in this collection process. Also, the existing privacy-enhanced solutions that emerged in the last years follow an "all or nothing" approach, leaving the user the sole option to accept or completely deny the access to privacy-related data. This work has the two-fold objective of assessing the privacy implications on the usage of analytics libraries in mobile apps and proposing a data anonymization methodology that enables a trade-off between the utility and privacy of the collected data and gives the user complete control over the sharing process. To achieve that, we present an empirical privacy assessment on the analytics libraries contained in the 4500 most-used Android apps of the Google Play Store between November 2020 and January 2021. Then, we propose an empowered anonymization methodology, based on MobHide [1], that gives the end-user complete control over the collection and anonymization process. Finally, we empirically demonstrate the applicability and effectiveness of such anonymization methodology thanks to HideDroid, a fully-fledged anonymization app for the Android ecosystem.

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“…Local differential privacy (LDP) [114] offers stronger privacy guarantee, data owners perturb their private information to satisfy DP locally before reporting it to an untrusted data curator [86], [87], [116]. A comprehensive survey of LDP can be referred to [117]. A formal definition of LDP is given in Definition 5.2.…”

Section: Privacy-preservation Through Differential Privacymentioning

confidence: 99%

Privacy and Robustness in Federated Learning: Attacks and Defenses

Lyu¹,

Han²,

Ma³

et al. 2020

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As data are increasingly being stored in different silos and societies becoming more aware of data privacy issues, the traditional centralized training of artificial intelligence (AI) models is facing efficiency and privacy challenges. Recently, federated learning (FL) has emerged as an alternative solution and continue to thrive in this new reality. Existing FL protocol design has been shown to be vulnerable to adversaries within or outside of the system, compromising data privacy and system robustness. Besides training powerful global models, it is of paramount importance to design FL systems that have privacy guarantees and are resistant to different types of adversaries. In this paper, we conduct the first comprehensive survey on this topic. Through a concise introduction to the concept of FL, and a unique taxonomy covering: 1) threat models; 2) poisoning attacks and defenses against robustness; 3) inference attacks and defenses against privacy, we provide an accessible review of this important topic. We highlight the intuitions, key techniques as well as fundamental assumptions adopted by various attacks and defenses. Finally, we discuss promising future research directions towards robust and privacy-preserving federated learning.

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Local Differential Privacy and Its Applications: A Comprehensive Survey

Cited by 35 publications

References 132 publications

Fair and Differentially Private Distributed Frequency Estimation

Fair and Differentially Private Distributed Frequency Estimation

You can't always get what you want: towards user-controlled privacy on Android

Privacy and Robustness in Federated Learning: Attacks and Defenses

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