CRFeb 4, 2022

With a Little Help from My Friends: Transport Deniability for Instant Messaging

arXiv:2202.02043v1
Originality Incremental advance
AI Analysis

This addresses privacy risks for IM users by enabling deniable messaging with low overhead, though it is an incremental step as it builds on existing hybrid models.

The paper tackles the privacy challenge of traffic analysis in instant messaging by proposing DenIM, a protocol that makes deniable messages indistinguishable from regular ones with help from friends, achieving overhead proportional to messages sent and showing effectiveness against strong adversaries like ISPs.

Traffic analysis for instant messaging (IM) applications continues to pose an important privacy challenge. In particular, transport-level data can leak unintentional information about IM -- such as who communicates with whom. Existing tools for metadata privacy have adoption obstacles, including the risks of being scrutinized for having a particular app installed, and performance overheads incompatible with mobile devices. We posit that resilience to traffic analysis must be directly supported by major IM services themselves, and must be done in a low-cost manner without breaking existing features. As a first step in this direction, we propose a hybrid messaging model that combines regular and deniable messages. We present a novel protocol for deniable instant messaging, which we call DenIM. DenIM is built on the principle that deniable messages can be made indistinguishable from regular messages with a little help from a user's friends. Deniable messages' network traffic can then be explained by a plausible cover story. DenIM achieves overhead proportional to the messages sent, as opposed to scaling with time or number of users. To show the effectiveness of DenIM, we implement a trace simulator, and show that DenIM's deniability guarantees hold against strong adversaries such as internet service providers.

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