Zachary Ratliff (Harvard University), Ruoxing (David) Yang (Georgetown University), Avery Bai (Georgetown University), Harel Berger (Ariel University), Micah Sherr (Georgetown University), James Mickens (Harvard University)

In authoritarian and highly surveilled environments, traditional communication networks are vulnerable to censorship, monitoring, and disruption. While decentralized anonymity networks such as Tor provide strong privacy guarantees, they remain dependent on centralized Internet infrastructure, making them susceptible to large-scale blocking or shutdowns. To address these limitations, we present MIRAGE, a privacy-preserving mobility-based messaging system designed for censorship-resistant communication. MIRAGE uses a district-based routing scheme that probabilistically forwards messages based on the high-level mobility patterns of the population. To prevent leakage of individual mobility behavior, MIRAGE protects users’ mobility patterns with local differential privacy, ensuring that participation in the network does not reveal an individual’s location history through observable routing decisions.

We implement MIRAGE within Cadence, an open-source simulator that provides a unified framework for evaluating mobility-based protocols using approximated geographical encounters between nodes over time. We analyze the privacy and efficiency tradeoffs of MIRAGE and evaluate its performance against (1) traditional epidemic and random-walk-based routing protocols and (2) the state-of-the-art privacy-preserving geography-based routing protocol, using real-world trajectories—one from pedestrian movement patterns collected in various urban locations and another consisting of GPS traces from taxi operations. Our results demonstrate that MIRAGE significantly reduces message overhead compared to epidemic routing, and outperforms probabilistic flooding in terms of delivery rate, while providing stronger privacy guarantees than existing techniques.

View More Papers

FidelityGPT: Correcting Decompilation Distortions with Retrieval Augmented Generation

Zhiping Zhou (Tianjin University), Xiaohong Li (Tianjin University), Ruitao Feng (Southern Cross University), Yao Zhang (Tianjin University), Yuekang Li (University of New South Wales), Wenbu Feng (Tianjin University), Yunqian Wang (Tianjin University), Yuqing Li (Tianjin University)

Read More

Evaluating Impact of Coverage Feedback on Estimators for Maximum...

Nelum Attanayake (School of Computer Science, University of Sydney), Danushka Liyanage (School of Computer Science, University of Sydney), Clement Canonne (School of Computer Science, University of Sydney), Suranga Seneviratne (School of Computer Science, University of Sydney), Rahul Gopinath (School of Computer Science, University of Sydney)

Read More