Simon Langowski (Massachusetts Institute of Technology), Sacha Servan-Schreiber (Massachusetts Institute of Technology), Srinivas Devadas (Massachusetts Institute of Technology)

Trellis is a mix-net based anonymous broadcast system with cryptographic security guarantees. Trellis can be used to anonymously publish documents or communicate with other users, all while assuming full network surveillance. In Trellis, users send messages through a set of servers in successive rounds. The servers mix and post the messages to a public bulletin board, hiding which users sent which messages.

Trellis hides all network-level metadata, remains robust to changing network conditions, guarantees availability to honest users, and scales with the number of mix servers. Trellis provides three to five orders of magnitude faster performance and better network robustness compared to Atom, the state-of-the-art anonymous broadcast system with a similar threat model. In achieving these guarantees, Trellis contributes: (1) a simpler theoretical mixing analysis for a routing mix network constructed with a fraction of malicious servers, (2) anonymous routing tokens for verifiable random paths, and (3) lightweight blame protocols built on top of onion routing to identify and eliminate malicious parties.

We implement and evaluate Trellis in a networked deployment. With 64 servers located across four geographic regions, Trellis achieves a throughput of 220 bits per second with 100,000 users. With 128 servers, Trellis achieves a throughput of 320 bits per second. Trellis’s throughput is only 100 to 1000× slower compared to Tor (which has 6,000 servers and 2M daily users) and is therefore potentially deployable at a smaller “enterprise” scale. Our implementation is open-source.

View More Papers

Post-GDPR Threat Hunting on Android Phones: Dissecting OS-level Safeguards...

Mark Huasong Meng (National University of Singapore), Qing Zhang (ByteDance), Guangshuai Xia (ByteDance), Yuwei Zheng (ByteDance), Yanjun Zhang (The University of Queensland), Guangdong Bai (The University of Queensland), Zhi Liu (ByteDance), Sin G. Teo (Agency for Science, Technology and Research), Jin Song Dong (National University of Singapore)

Read More

MetaWave: Attacking mmWave Sensing with Meta-material-enhanced Tags

Xingyu Chen (University of Colorado Denver), Zhengxiong Li (University of Colorado Denver), Baicheng Chen (University of California San Diego), Yi Zhu (SUNY at Buffalo), Chris Xiaoxuan Lu (University of Edinburgh), Zhengyu Peng (Aptiv), Feng Lin (Zhejiang University), Wenyao Xu (SUNY Buffalo), Kui Ren (Zhejiang University), Chunming Qiao (SUNY at Buffalo)

Read More

Ethical Challenges in Blockchain Network Measurement Research

Yuzhe Tang (Syracuse University), Kai Li (San Diego State University), and Yibo Wang and Jiaqi Chen (Syracuse University)

Read More