Hiding the Rumor Source

TL;DR

This paper introduces adaptive diffusion, a new messaging protocol that effectively conceals the source of a message in anonymous social networks, even against adversaries with snapshot or timestamp data, by leveraging properties of regular and irregular trees.

Contribution

The paper proposes adaptive diffusion, a novel protocol that enhances source anonymity in social networks, with theoretical analysis and empirical validation on real-world network data.

Findings

Achieves perfect source obfuscation on infinite regular trees.

Effectively hides message origin on irregular and finite networks.

Maintains rapid message spread while preserving anonymity.

Abstract

Anonymous social media platforms like Secret, Yik Yak, and Whisper have emerged as important tools for sharing ideas without the fear of judgment. Such anonymous platforms are also important in nations under authoritarian rule, where freedom of expression and the personal safety of message authors may depend on anonymity. Whether for fear of judgment or retribution, it is sometimes crucial to hide the identities of users who post sensitive messages. In this paper, we consider a global adversary who wishes to identify the author of a message; it observes either a snapshot of the spread of a message at a certain time, sampled timestamp metadata, or both. Recent advances in rumor source detection show that existing messaging protocols are vulnerable against such an adversary. We introduce a novel messaging protocol, which we call adaptive diffusion, and show that under the snapshot adversarial model, adaptive diffusion spreads content fast and achieves perfect obfuscation of the source when the underlying contact network is an infinite regular tree. That is, all users with the message are nearly equally likely to have been the origin of the message. When the contact network is an irregular tree, we characterize the probability of maximum likelihood detection by proving a concentration result over Galton-Watson trees. Experiments on a sampled Facebook network demonstrate that adaptive diffusion effectively hides the location of the source even when the graph is finite, irregular and has cycles.