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

CTng: Secure Certificate and Revocation Transparency

Jie Kong (Dept. of Computer Science and Engineering, University of Connecticut, Storrs, CT), Damon James (Dept. of Computer Science and Engineering, University of Connecticut, Storrs, CT), Hemi Leibowitz (Faculty of Computer Science, The College of Management Academic Studies, Rishon LeZion, Israel), Ewa Syta (Dept. of Computer Science, Trinity College, Hartford, CT), Amir Herzberg (Dept. of…

Read More

DOM-XSS Detection via Webpage Interaction Fuzzing and URL Component...

Nuno Sabino (Carnegie Mellon University, Instituto Superior Técnico, Universidade de Lisboa, and Instituto de Telecomunicações), Darion Cassel (Carnegie Mellon University), Rui Abreu (Universidade do Porto, INESC-ID), Pedro Adão (Instituto Superior Técnico, Universidade de Lisboa, and Instituto de Telecomunicações), Lujo Bauer (Carnegie Mellon University), Limin Jia (Carnegie Mellon University)

Read More

Analysing Privacy Risks in Children’s Educational Apps in Australia

Sicheng Jin (University of New South Wales), Rahat Masood (University of New South Wales), Jung-Sook Lee (University of New South Wales), Hye-Young (Helen) Paik (University of New South Wales)

Read More