Evaluation of Open-Source Tools for Differential Privacy

作     者：Zhang, Shiliang Hagermalm, Anton Slavnic, Sanjin Schiller, Elad Michael Almgren, Magnus 

作者机构：Chalmers Univ Technol Comp Sci & Engn SE-41296 Gothenburg Sweden Univ Oslo Dept Informat NO-0373 Oslo Norway 

出 版 物：《SENSORS》 (传感器)

年 卷 期：2023年第23卷第14期

页      面：6509-6509页

核心收录：

学科分类：0710[理学-生物学] 071010[理学-生物化学与分子生物学] 0808[工学-电气工程] 07[理学] 0804[工学-仪器科学与技术] 0703[理学-化学] 

基　　金：project Privacy-Protected Machine Learning for Transport Systems' of Area of Advance Transport and Chalmers AI Research Centre (CHAIR) AutoSPADA (Automotive Stream Processing and Distributed Analytics) OODIDA Phase 2 by Vinnova [2019-05884] AstraZeneca AB Swedish Research Council [2018-05973] Vinnova [2019-05884] Funding Source: Vinnova 

主　　题：differential privacy open-source tools evaluation 

摘      要：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.