PRIVEE: A Visual Analytic Workflow for Proactive Privacy Risk Inspection of Open Data.

ABSTRACT: Open data sets that contain personal information are susceptible to adversarial attacks even when anonymized. By performing low-cost joins on multiple datasets with shared attributes, malicious users of open data portals might get access to information that violates individuals' privacy. However, open data sets are primarily published using a release-and-forget model, whereby data owners and custodians have little to no cognizance of these privacy risks. We address this critical gap by developing a visual analytic solution that enables data defenders to gain awareness about the disclosure risks in local, joinable data neighborhoods. The solution is derived through a design study with data privacy researchers, where we initially play the role of a red team and engage in an ethical data hack

SUBMITTER: Bhattacharjee K 

PROVIDER: S-EPMC9841578 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature