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

TL;DR

PRIVEE is a visual analytic tool designed to help data defenders proactively identify and interpret privacy risks in open datasets, especially those vulnerable to malicious joins, by providing interactive visualizations and risk assessments.

Contribution

The paper introduces PRIVEE, a novel visual analytic workflow that enables proactive privacy risk inspection in open data, developed through a design study with privacy researchers.

Findings

PRIVEE effectively identifies vulnerable data joins and privacy risks.

The tool helps simulate attack scenarios for better risk understanding.

Case studies demonstrate its utility in real-world privacy assessments.

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 hacking exercise based on privacy attack scenarios. We use this problem and domain characterization to develop a set of visual analytic interventions as a defense mechanism and realize them in PRIVEE, a visual risk inspection workflow that acts as a proactive monitor for data defenders. PRIVEE uses a combination of risk scores and associated interactive visualizations to let data defenders explore vulnerable joins and interpret risks at multiple levels of data granularity. We demonstrate how PRIVEE can help emulate the attack strategies and diagnose disclosure risks through two case studies with data privacy experts.