Heterogeneous differential privacy for vertically partitioned databases. (20th December 2019)

• Record Type: Journal Article
• Authors: Xia, Yang; Zhu, Tianqing; Ding, Xiaofeng; Jin, Hai; Zou, Deqing
• Is Part Of: Concurrency and computation. Volume 33:Number 8(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-12-20
• Subjects: differential privacy -- heterogeneous privacy -- privacy label -- vertically partitioned data; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5607
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Existing privacy‐preserving approaches are generally designed to provide privacy guarantee for individual data in a database, which reduces the utility of the database for data analysis. In this paper, we propose a novel differential privacy mechanism to preserve the heterogeneous privacy of a vertically partitioned database based on attributes. We first present the concept of privacy label, which characterizes the privacy information of the database and is instantiated by the classification. Then, we use an information‐based method to systematically explore the dependencies between all attributes and the privacy label. We finally assign privacy weights to every attribute and design a heterogeneous mechanism according to the basic Laplace mechanism. Evaluations using real datasets demonstrate that the proposed mechanism achieves a balanced privacy and utility.