Open AccessProceedings Article
Locally Differentially Private Protocols for Frequency Estimation
Tianhao Wang,Jeremiah Blocki,Ninghui Li,Somesh Jha +3 more
- pp 729-745
TLDR
This paper introduces a framework that generalizes several LDP protocols proposed in the literature and yields a simple and fast aggregation algorithm, whose accuracy can be precisely analyzed, resulting in two new protocols that provide better utility than protocols previously proposed.Abstract:
Protocols satisfying Local Differential Privacy (LDP) enable parties to collect aggregate information about a population while protecting each user’s privacy, without relying on a trusted third party. LDP protocols (such as Google’s RAPPOR) have been deployed in real-world scenarios. In these protocols, a user encodes his private information and perturbs the encoded value locally before sending it to an aggregator, who combines values that users contribute to infer statistics about the population. In this paper, we introduce a framework that generalizes several LDP protocols proposed in the literature. Our framework yields a simple and fast aggregation algorithm, whose accuracy can be precisely analyzed. Our in-depth analysis enables us to choose optimal parameters, resulting in two new protocols (i.e., Optimized Unary Encoding and Optimized Local Hashing) that provide better utility than protocols previously proposed. We present precise conditions for when each proposed protocol should be used, and perform experiments that demonstrate the advantage of our proposed protocols.read more
Citations
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Privacy-Preserved Data Sharing Towards Multiple Parties in Industrial IoTs
Xu Zheng,Zhipeng Cai +1 more
TL;DR: This paper proposes a privacy-preserved data sharing framework for IIoTs, where multiple competing data consumers exist in different stages of the system, and provides for both algorithms a comprehensive consideration on privacy, data utility, bandwidth efficiency, payment, and rationality for data sharing.
Proceedings ArticleDOI
Prochlo: Strong Privacy for Analytics in the Crowd
Andrea Bittau,Úlfar Erlingsson,Petros Maniatis,Ilya Mironov,Ananth Raghunathan,David Lie,Mitch Rudominer,Usharsee Kode,Julien Tinnes,Bernhard Seefeld +9 more
TL;DR: Encode, Shuffle, Analyze (ESA) as discussed by the authors is a principled system architecture for performing large-scale monitoring of computer users' software activities with high utility while also protecting user privacy.
Posted Content
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity
Úlfar Erlingsson,Vitaly Feldman,Ilya Mironov,Ananth Raghunathan,Kunal Talwar,Abhradeep Thakurta +5 more
TL;DR: It is shown, via a new and general privacy amplification technique, that any permutation-invariant algorithm satisfying e-local differential privacy will satisfy [MATH HERE]-central differential privacy.
Proceedings ArticleDOI
Collecting and Analyzing Multidimensional Data with Local Differential Privacy
TL;DR: Li et al. as discussed by the authors proposed novel LDP mechanisms for collecting a numeric attribute, whose accuracy is at least no worse (and usually better) than existing solutions in terms of worst-case noise variance.
Proceedings ArticleDOI
Privacy at Scale: Local Differential Privacy in Practice
TL;DR: This tutorial aims to introduce the key technical underpinnings of these deployed LDP systems, to survey current research that addresses related problems within the LDP model, and to identify relevant open problems and research directions for the community.
References
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