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

PeriScope: An Effective Probing and Fuzzing Framework for the...

Dokyung Song (University of California, Irvine), Felicitas Hetzelt (Technical University of Berlin), Dipanjan Das (University of California, Santa Barbara), Chad Spensky (University of California, Santa Barbara), Yeoul Na (University of California, Irvine), Stijn Volckaert (University of California, Irvine and KU Leuven), Giovanni Vigna (University of California, Santa Barbara), Christopher Kruegel (University of California, Santa Barbara),…

Read More

One Engine To Serve 'em All: Inferring Taint Rules...

Zheng Leong Chua (National University of Singapore), Yanhao Wang (TCA/SKLCS, Institute of Software, Chinese Academy of Sciences), Teodora Baluta (National University of Singapore), Prateek Saxena (National University of Singapore), Zhenkai Liang (National University of Singapore), Purui Su (TCA/SKLCS, Institute of Software, Chinese Academy of Sciences)

Read More

Neural Machine Translation Inspired Binary Code Similarity Comparison beyond...

Fei Zuo (University of South Carolina), Xiaopeng Li (University of South Carolina), Patrick Young (Temple University), Lannan Luo (University of South Carolina), Qiang Zeng (University of South Carolina), Zhexin Zhang (University of South Carolina)

Read More

SANCTUARY: ARMing TrustZone with User-space Enclaves

Ferdinand Brasser (Technische Universität Darmstadt), David Gens (Technische Universität Darmstadt), Patrick Jauernig (Technische Universität Darmstadt), Ahmad-Reza Sadeghi (Technische Universität Darmstadt), Emmanuel Stapf (Technische Universität Darmstadt)

Read More