Amit Klein (Bar Ilan University), Benny Pinkas (Bar Ilan University)

We describe a novel user tracking technique that is based on assigning statistically unique DNS records per user. This new tracking technique is unique in being able to distinguish between machines that have identical hardware and software, and track users even if they use “privacy mode” browsing, or use multiple browsers (on the same machine).
The technique overcomes issues related to the caching of DNS answers in resolvers, and utilizes per-device caching of DNS answers at the client. We experimentally demonstrate that it covers the technologies used by a very large fraction of Internet users (in terms of browsers, operating systems, and DNS resolution platforms).
Our technique can track users for up to a day (typically), and therefore works best when combined with other, narrower yet longer-lived techniques such as regular cookies - we briefly
explain how to combine such techniques.
We suggest mitigations to this tracking technique but note that it is not easily mitigated. There are possible workarounds, yet these are not without setup overhead, performance overhead or convenience overhead. A complete mitigation requires software modifications in both browsers and resolver software.

View More Papers

Profit: Detecting and Quantifying Side Channels in Networked Applications

Nicolás Rosner (University of California, Santa Barbara), Ismet Burak Kadron (University of California, Santa Barbara), Lucas Bang (Harvey Mudd College), Tevfik Bultan (University of California, Santa Barbara)

Read More

Coconut: Threshold Issuance Selective Disclosure Credentials with Applications to...

Alberto Sonnino (University College London (UCL)), Mustafa Al-Bassam (University College London (UCL)), Shehar Bano (University College London (UCL)), Sarah Meiklejohn (University College London (UCL)), George Danezis (University College London (UCL))

Read More

ExSpectre: Hiding Malware in Speculative Execution

Jack Wampler (University of Colorado Boulder), Ian Martiny (University of Colorado Boulder), Eric Wustrow (University of Colorado Boulder)

Read More

A Systematic Framework to Generate Invariants for Anomaly Detection...

Cheng Feng (Imperial College London & Siemens Corporate Technology), Venkata Reddy Palleti (Singapore University of Technology and Design), Aditya Mathur (Singapore University of Technology and Design), Deeph Chana (Imperial College London)

Read More