Diffusing Private Data over Networks

TL;DR

This paper introduces a privacy-preserving mechanism for diffusing private data over social networks, ensuring differential privacy based on user distance while allowing global data analysis.

Contribution

It proposes a novel differential privacy mechanism that accounts for network distance, balancing data utility and privacy in social network data sharing.

Findings

Mechanism guarantees privacy proportional to network distance.

Allows global statistical inference without compromising individual privacy.

Validated on synthetic GPS data and Facebook infection status data.

Abstract

The emergence of social and technological networks has enabled rapid sharing of data and information. This has resulted in significant privacy concerns where private information can be either leaked or inferred from public data. The problem is significantly harder for social networks where we may reveal more information to our friends than to strangers. Nonetheless, our private information can still leak to strangers as our friends are their friends and so on. In order to address this important challenge, in this paper, we present a privacy-preserving mechanism that enables private data to be diffused over a network. In particular, whenever a user wants to access another users' data, the proposed mechanism returns a differentially private response that ensures that the amount of private data leaked depends on the distance between the two users in the network. While allowing global statistics to be inferred by users acting as analysts, our mechanism guarantees that no individual user, or a group of users, can harm the privacy guarantees of any other user. We illustrate our mechanism with two examples: one on synthetic data where the users share their GPS coordinates; and one on a Facebook ego-network where a user shares her infection status.