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Title: | PrivKVM* : revisiting key-value statistics estimation with local differential privacy | Authors: | Ye, Q Hu, H Meng, X Zheng, H Huang, K Fang, C Shi, J |
Issue Date: | Jan-2023 | Source: | IEEE transactions on dependable and secure computing, Jan.-Feb. 2023, v. 20, no. 1, p. 17-35 | Abstract: | A key factor in big data analytics and artificial intelligence is the collection of user data from a large population. However, the collection of user data comes at the price of privacy risks, not only for users but also for businesses who are vulnerable to internal and external data breaches. To address privacy issues, local differential privacy (LDP) has been proposed to enable an untrusted collector to obtain accurate statistical estimation on sensitive user data (e.g., location, health, and financial data) without actually accessing the true records. As key-value data is an extremely popular NoSQL data model, there are a few works in the literature that study LDP-based statistical estimation on key-value data. However, these works have some major limitations, including supporting small key space only, fixed key collection range, difficulty in choosing an appropriate padding length, and high communication cost. In this article, we propose a two-phase mechanism PrivKVM∗ as an optimized and highly-complete solution to LDP-based key-value data collection and statistics estimation. We verify its correctness and effectiveness through rigorous theoretical analysis and extensive experimental results. | Keywords: | Histogram Key-value data Local differential privacy Privacy-preserving data collection Statistics estimation |
Publisher: | Institute of Electrical and Electronics Engineers | Journal: | IEEE transactions on dependable and secure computing | ISSN: | 1545-5971 | EISSN: | 1941-0018 | DOI: | 10.1109/TDSC.2021.3107512 | Rights: | © 2021 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 et al., "PrivKVM*: Revisiting Key-Value Statistics Estimation With Local Differential Privacy," in IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 1, pp. 17-35, Jan.-Feb. 2023 is available at https://doi.org/10.1109/TDSC.2021.3107512. |
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Ye_Privkvm_Revisiting_Key-Value.pdf | Pre-Published version | 6.06 MB | Adobe PDF | View/Open |
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