Evaluation of Open-Source Tools for Differential Privacy
Artikel i vetenskaplig tidskrift, 2023

Differential privacy (DP) defines privacy protection by promising quantified indistinguishability between individuals who consent to share their privacy-sensitive information and those who do not. DP aims to deliver this promise by including well-crafted elements of random noise in the published data, and thus there is an inherent tradeoff between the degree of privacy protection and the ability to utilize the protected data. Currently, several open-source tools have been proposed for DP provision. To the best of our knowledge, there is no comprehensive study for comparing these open-source tools with respect to their ability to balance DP's inherent tradeoff as well as the use of system resources. This work proposes an open-source evaluation framework for privacy protection solutions and offers evaluation for OpenDP Smartnoise, Google DP, PyTorch Opacus, Tensorflow Privacy, and Diffprivlib. In addition to studying their ability to balance the above tradeoff, we consider discrete and continuous attributes by quantifying their performance under different data sizes. Our results reveal several patterns that developers should have in mind when selecting tools under different application needs and criteria. This evaluation survey can be the basis for an improved selection of open-source DP tools and quicker adaptation of DP.

open-source tools

differential privacy

evaluation

Författare

Shiliang Zhang

Universitetet i Oslo

Nätverk och System

Anton Hagermalm

Student vid Chalmers

Sanjin Slavnic

Student vid Chalmers

Elad Schiller

Nätverk och System

Magnus Almgren

Nätverk och System

Sensors

14248220 (eISSN)

Vol. 23 14 6509

AutoSPADA (Automotive Stream Processing and Distributed Analytics) OODIDA Phase 2

VINNOVA (2019-05884), 2020-03-12 -- 2022-12-31.

Styrkeområden

Informations- och kommunikationsteknik

Ämneskategorier

Datavetenskap (datalogi)

DOI

10.3390/s23146509

PubMed

37514803

Mer information

Senast uppdaterat

2024-04-23