From Closed-world Enforcement to Open-world Assessment of Privacy

TL;DR

This paper introduces a user-centric privacy assessment framework for open settings, addressing challenges like heterogeneous information dissemination and context-dependent privacy needs, and validates it through large-scale Reddit data analysis.

Contribution

It formalizes privacy requirements for open environments, proposes the d-convergence measure for identity linkability, and demonstrates its effectiveness on real-world social media data.

Findings

d-convergence effectively measures entity linkability

The framework reveals insights into data structure and privacy risks

Non-disclosure guarantees are impossible in open settings

Abstract

In this paper, we develop a user-centric privacy framework for quantitatively assessing the exposure of personal information in open settings. Our formalization addresses key-challenges posed by such open settings, such as the unstructured dissemination of heterogeneous information and the necessity of user- and context-dependent privacy requirements. We propose a new definition of information sensitivity derived from our formalization of privacy requirements, and, as a sanity check, show that hard non-disclosure guarantees are impossible to achieve in open settings. After that, we provide an instantiation of our framework to address the identity disclosure problem, leading to the novel notion of d-convergence. d-convergence is based on indistinguishability of entities and it bounds the likelihood with which an adversary successfully links two profiles of the same user across online communities. Finally, we provide a large-scale evaluation of our framework on a collection of 15 million comments collected from the Online Social Network Reddit. Our evaluation validates the notion of d-convergence for assessing the linkability of entities in our data set and provides deeper insights into the data set's structure.