Anonymous messaging platforms allow users to spread messages over a network (e.g., a social network) without revealing message authorship to other users. Popular demand for anonymous messaging is evidenced by the success of mobile apps like Whisper and Yik Yak. In such platforms, the spread of messages is typically modeled as a diffusion process. Recent advances in network analysis have revealed that such diffusion processes are vulnerable to author deanonymization by adversaries with access to metadata, such as timing information. In this work, we ask the fundamental question of how to intervene in the propagation of anonymous messages in order to make it difficult to find the source. In particular, we study the performance of a message propagation protocol called adaptive diffusion introduced in  [1] . We prove that it achieves asymptotically optimal source-hiding and significantly outperforms standard diffusion. We further demonstrate empirically that adaptive diffusion hides the source effectively on real social graphs.
['Giulia C. Fanti', 'Peter Kairouz', 'Sewoong Oh', 'Kannan Ramchandran', 'Pramod Viswanath']
Metadata-Conscious Anonymous Messaging