DPKnob: A visual analysis approach to risk-aware formulation of differential privacy schemes for data query scenarios

Jiao, Shuangcheng1; Cheng, Jiang2; Huang, Zhaosong3; Li, Tong1; Xie, Tiankai4; Chen, Wei3; Ma, Yuxin5; Wang, Xumeng1

2024-09-01

10.1016/j.visinf.2024.09.002

VISUAL INFORMATICS   影响因子和分区

2468-502X

Differential privacy is an essential approach for privacy preservation in data queries. However, users face a significant challenge in selecting an appropriate privacy scheme, as they struggle to balance the utility of query results with the preservation of diverse individual privacy. Customizing a privacy scheme becomes even more complex in dealing with queries that involve multiple data attributes. When adversaries attempt to breach privacy firewalls by conducting multiple regular data queries with various attribute values, data owners must arduously discern unpredictable disclosure risks and construct suitable privacy schemes. In this paper, we propose a visual analysis approach for formulating privacy schemes of differential privacy. Our approach supports the identification and simulation of potential privacy attacks in querying statistical results of multi-dimensional databases. We also developed a prototype system, called DPKnob, which integrates multiple coordinated views. DPKnob not only allows users to interactively assess and explore privacy exposure risks by browsing high- risk attacks, but also facilitates an iterative process for formulating and optimizing privacy schemes based on differential privacy. This iterative process allows users to compare different schemes, refine their expectations of privacy and utility, and ultimately establish a well-balanced privacy scheme. The effectiveness of this study is verified by a user study and two case studies with real-world datasets. (c) 2024 The Authors. Published by Elsevier B.V. on behalf of Zhejiang University and Zhejiang University Press Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Visual analytics Human-centered computing Differential privacy

ESCI

英语

通讯

NSFC, China["62202244","U22B2034"]

Computer Science

Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications ; Computer Science, Software Engineering

WOS:001328200900001

ELSEVIER

Web of Science

1.Nankai Univ, DISSec, Tianjin, Peoples R China
2.China Mobile Commun Grp ZheJiang Co Ltd, Hangzhou, Zhejiang, Peoples R China
3.Zhejiang Univ, State Key Lab CAD&CG, Hangzhou, Zhejiang, Peoples R China
4.Arizona State Univ, Tempe, AZ USA
5.Southern Univ Sci & Technol, Shenzhen, Guangdong, Peoples R China

Jiao, Shuangcheng,Cheng, Jiang,Huang, Zhaosong,et al. DPKnob: A visual analysis approach to risk-aware formulation of differential privacy schemes for data query scenarios[J]. VISUAL INFORMATICS,2024,8(3).

Jiao, Shuangcheng.,Cheng, Jiang.,Huang, Zhaosong.,Li, Tong.,Xie, Tiankai.,...&Wang, Xumeng.(2024).DPKnob: A visual analysis approach to risk-aware formulation of differential privacy schemes for data query scenarios.VISUAL INFORMATICS,8(3).

Jiao, Shuangcheng,et al."DPKnob: A visual analysis approach to risk-aware formulation of differential privacy schemes for data query scenarios".VISUAL INFORMATICS 8.3(2024).