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Title: PrivKV : key-value data collection with local differential privacy
Authors: Ye, Q 
Hu, H 
Meng, X
Zheng, H 
Issue Date: 2019
Source: Proceedings - IEEE Symposium on Security and Privacy, 19-23 May 2019, San Francisco, CA, USA, p. 317-331
Abstract: Local differential privacy (LDP), where each user perturbs her data locally before sending to an untrusted data collector, is a new and promising technique for privacy-preserving distributed data collection. The advantage of LDP is to enable the collector to obtain accurate statistical estimation on sensitive user data (e.g., location and app usage) without accessing them. However, existing work on LDP is limited to simple data types, such as categorical, numerical, and set-valued data. To the best of our knowledge, there is no existing LDP work on key-value data, which is an extremely popular NoSQL data model and the generalized form of set-valued and numerical data. In this paper, we study this problem of frequency and mean estimation on key-value data by first designing a baseline approach PrivKV within the same 'perturbation-calibration' paradigm as existing LDP techniques. To address the poor estimation accuracy due to the clueless perturbation of users, we then propose two iterative solutions PrivKVM and PrivKVM+ that can gradually improve the estimation results through a series of iterations. An optimization strategy is also presented to reduce network latency and increase estimation accuracy by introducing virtual iterations in the collector side without user involvement. We verify the correctness and effectiveness of these solutions through theoretical analysis and extensive experimental results.
Keywords: Data-perturbation
Key-value-data
Local-differential-privacy
Publisher: Institute of Electrical and Electronics Engineers
ISBN: 978-1-5386-6660-9 (Electronic)
978-1-5386-6661-6 (Print on Demand(PoD))
DOI: 10.1109/SP.2019.00018
Rights: © 2019, Qingqing Ye. Under license to IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
The following publication Q. Ye, H. Hu, X. Meng and H. Zheng, "PrivKV: Key-Value Data Collection with Local Differential Privacy," 2019 IEEE Symposium on Security and Privacy (SP), San Francisco, CA, USA, 2019, pp. 317-331 is available at https://dx.doi.org/10.1109/SP.2019.00018.
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