Privacy Preserving Detection of Path Bias Attacks in Tor

Lauren Watson, Anupam Mediratta, Mohammad Tariq Elahi, Rik Sarkar

Research output: Contribution to journalArticlepeer-review

Abstract

Anonymous communication networks like Tor are vulnerable to attackers that control entry and exit nodes. Such attackers can compromise the essential anonymity and privacy properties of the network. In this paper, we consider the path bias attack – where the attacker induces a client to use compromised nodes and thus links the client to their destination. We describe an efficient scheme that detects such attacks in Tor by collecting routing telemetry data from nodes in the network. The data collection is differentially private and thus does not reveal behaviour of individual users even to nodes within the network. We show provable bounds for the sample complexity of the scheme and describe methods to make it resilient to introduction of false data by the attacker to subvert the detection process. Simulations based on real configurations of the Tor network show that the method works accurately in practice.
Original languageEnglish
Pages (from-to)111-130
Number of pages20
JournalWater Treatment Technology
Volume2020
Issue number4
Early online date17 Aug 2020
DOIs
Publication statusPublished - 1 Oct 2020
Event20th Privacy Enhancing Technologies Symposium - Online
Duration: 13 Jul 202017 Jul 2020
https://petsymposium.org/2020/

Keywords

  • differential privacy
  • outlier detection
  • privacy preserving analysis

Fingerprint Dive into the research topics of 'Privacy Preserving Detection of Path Bias Attacks in Tor'. Together they form a unique fingerprint.

Cite this