Research

2026

1. 

   Persistent BitTorrent Trackers

   François-Xavier Wicht, Zhengwei Tong, Shunfan Zhou, Hang Yin, and Aviv Yaish

   In 2026 IEEE 11th European Symposium on Security and Privacy (EuroS&P), 2026

   Also presented at the Science of Blockchain Conference (SBC’26)

   Abs DOI arXiv PDF

   Private BitTorrent trackers enforce upload-to-download ratios to prevent free-riding, but suffer from three critical weaknesses: reputation cannot move between trackers, centralized servers create single points of failure, and upload statistics are self-reported and unverifiable. When a tracker shuts down (whether by operator choice, technical failure, or legal action) users lose their contribution history and cannot prove their standing to new communities. We address these problems by storing reputation in smart contracts and replacing self-reports with cryptographic attestations. Receiving peers sign receipts for transferred pieces, which the tracker aggregates and verifies before updating on-chain reputation. Trackers run in Trusted Execution Environments (TEEs) to guarantee correct aggregation and prevent manipulation of state. If a tracker is unavailable, peers use an authenticated Distributed Hash Table (DHT) for discovery: the on-chain reputation acts as a Public Key Infrastructure (PKI), so peers can verify each other and maintain access control without the tracker. This design persists reputation across tracker failures and makes it portable to new instances through single-hop migration in factory-deployed contracts. We formalize the security requirements, prove correctness under standard cryptographic assumptions, and evaluate a prototype on Intel TDX. Measurements show that transfer receipts add less than 6% overhead and signature aggregation speeds up verification by 2.5×.

2. 

   Privacy-Preserving Inclusion Lists

   Zhengwei Tong, Saba Eskandarian, and Kartik Nayak

   In Proc. 8th Conference on Advances in Financial Technologies (AFT 2026), 2026

   Abs PDF

   Blockchains aim to provide open access and censorship resistance, but centralization of block production in blockchains like Ethereum undermines these goals. Inclusion list protocols have been proposed to mitigate censorship by requiring block proposers to include transactions proposed by a committee of inclusion list (IL) proposers. However, protecting the privacy of these proposers is essential to prevent retaliation and to ensure robust censorship resistance. We propose a lightweight, privacy-preserving inclusion list protocol that allows committees to collectively construct transaction lists while hiding individual contributions and ensuring plausible deniability. Our approach builds on multiparty computation (MPC) techniques to achieve strong privacy without relying on heavyweight cryptography or anonymous channels. We implement two variants of these protocols that support a third of the parties behaving adversarially: an optimistic version that supports malicious security with abort, and a robust MPC that tolerates arbitrary faults. Under a default parameterization for inclusion lists being considered today, we observe a latency of ∼5.1s and ∼432.7s in the two variants.

3. Diffusion-Based Data-Driven Assortment Optimization

   Junyi Liao, Xiaohui Jiang, Zhengwei Tong, Ethan X. Fang, and Vahid Tarokh

   Under submission , 2026

   DOI arXiv PDF

2025

1. 

   MJ-Bench: Is Your Multimodal Reward Model Really a Good Judge for Text-to-Image Generation?

   Zhaorun Chen, Zichen Wen, Yichao Du, Yiyang Zhou, Chenhang Cui, Siwei Han, Zhenzhen Weng, Chaoqi Wang, Zhengwei Tong, Leria Huang, Canyu Chen, Haoqin Tu, Qinghao Ye, Zhihong Zhu, Yuqing Zhang, Jiawei Zhou, Zhuokai Zhao, Rafael Rafailov, Chelsea Finn, and Huaxiu Yao

   In NeurIPS 2025 Datasets and Benchmarks Track, 2025

   Poster

   HTML PDF Code