SoK: Chasing Accuracy and Privacy, and Catching Both in Differentially Private Histogram Publication
Journal article, 2020
Such a strong notion of privacy is differential privacy, a statistical notion of privacy that makes privacy leakage quantifiable. The caveat regarding differential privacy is that while it has strong guarantees for privacy, privacy comes at a cost of accuracy. Despite this trade-off being a central and important issue in the adoption of differential privacy, there exists a gap in the literature regarding providing an understanding of the trade-off and how to address it appropriately.
Through a systematic literature review (SLR), we investigate the state-of-the-art within accuracy improving differentially private algorithms for histogram and synthetic data publishing. Our contribution is two-fold: 1) we identify trends and connections in the contributions to the field of differential privacy for histograms and synthetic data and 2) we provide an understanding of the privacy/accuracy trade-off challenge by crystallizing different dimensions to accuracy improvement. Accordingly, we position and visualize the ideas in relation to each other and external work, and deconstruct each algorithm to examine the building blocks separately with the aim of pinpointing which dimension of accuracy improvement each technique/approach is targeting. Hence, this systematization of knowledge (SoK) provides an understanding of in which dimensions and how accuracy improvement can be pursued without sacrificing privacy.
systematic literature review
systematization of knowledge
Chalmers, Computer Science and Engineering (Chalmers), Information Security
Transactions on Data Privacy
1888-5063 (ISSN) 2013-1631 (eISSN)Vol. 13 3 201-245
WebSec: Securing Web-driven Systems
Swedish Foundation for Strategic Research (SSF), 2018-03-01 -- 2023-02-28.
Other Computer and Information Science
Areas of Advance
Information and Communication Technology