Full Citation

Title: Event-set differential privacy for fine-grained data privacy protection

Citation Type: Journal Article

Publication Year: 2023

ISBN:

ISSN: 0925-2312

DOI: 10.1016/J.NEUCOM.2022.10.006

NSFID:

PMCID:

PMID:

Abstract: Privacy-preserving data statistics and analysis has become an urgent problem nowadays. Differential privacy (DP), as a rigorous privacy paradigm, has been widely adopted in various fields. However, in the context of large-scale mobile applications where each user has multiple records, both user-level DP and record-level DP cannot achieve a good compromise between stringent privacy and high data utility. A more satisfying privacy paradigm with desired granularity becomes very necessary. To this end, this paper proposes a fine-grained privacy paradigm called α-event-set differential privacy, which prevents adversaries from inferring any one of α event-sets owned by the user in data statistics and analysis. We theoretically introduce the definition, properties, and baseline mechanisms of α-event-set DP. Besides, we implement and evaluate α-event-set DP on mean estimation, histogram estimation, and machine learning applications, respectively. The experimental results have shown that α-event-set DP is able to achieve a fine-grained granularity of privacy protection while allowing high data utility.

Url: https://www.sciencedirect.com/science/article/pii/S0925231222012693#b0210

User Submitted?: No

Authors: Wang, Teng; Yang, Wanyun; Ma, Xin; Wang, Bin

Periodical (Full): Neurocomputing

Issue:

Volume: 515

Pages: 48-58

Data Collections: IPUMS International

Topics: Methodology and Data Collection

Countries: Brazil, Mexico