Paper 2021/374

ZXAD: Zero-knowledge Exit Abuse Detection for Tor

Akshaya Mani and Ian Goldberg

Abstract

The Tor anonymity network is often abused by some attackers to (anonymously) convey attack traffic. These attacks abuse Tor exit relays (i.e., the relays through which traffic exits Tor) by making it appear the attack originates there; as a result, many website operators indiscriminately block all Tor traffic (by blacklisting all exit IPs), reducing the usefulness of Tor. Recent research shows that majority of these attacks are ones that generate high traffic volume (e.g., Denial-of-Service attacks). This suggests that a simple solution such as throttling traffic flow at the Tor exits may permit early detection of these attacks. However, naively monitoring and throttling traffic at the Tor exits can endanger the privacy of the network's users. Indeed, many recent works have proposed private measurement systems that support safe aggregation of exit statistics. However, these systems do not permit identification of "unlinkable" connections that are part of a high-volume attack. Doing so could allow Tor to take proper remedial actions, such as dropping the attack traffic, but care must be taken to protect privacy. We present ZXAD (pronounced "zed-zad"), the first zero-knowledge based private Tor exit abuse detection system. ZXAD detects large-volume traffic attacks without revealing any information, apart from the fact that some user is conveying a high volume of traffic through Tor. We formally prove the correctness and security of ZXAD. We also measure two proof-of-concept implementations of our zero-knowledge proofs and show that ZXAD operates with low bandwidth and processing overheads.