Domain Name Encryption Is Not Enough: Privacy Leakage via IP-based Website Fingerprinting
Not on the map. Why?
Abstract (arXiv)
Although the security benefits of domain name encryption technologies such as DNS over TLS (DoT), DNS over HTTPS (DoH), and Encrypted Client Hello (ECH) are clear, their positive impact on user privacy is weakened by--the still exposed--IP address information. However, content delivery networks, DNS-based load balancing, co-hosting of different websites on the same server, and IP address churn, all contribute towards making domain-IP mappings unstable, and prevent straightforward IP-based browsing tracking. In this paper, we show that this instability is not a roadblock (assuming a universal DoT/DoH and ECH deployment), by introducing an IP-based website fingerprinting technique that allows a network-level observer to identify at scale the website a user visits. Our technique exploits the complex structure of most websites, which load resources from several domains besides their primary one. Using the generated fingerprints of more than 200K websites studied, we could successfully identify 84% of them when observing solely destination IP addresses. The accuracy rate increases to 92% for popular websites, and 95% for popular and sensitive websites. We also evaluated the robustness of the generated fingerprints over time, and demonstrate that they are still effective at successfully identifying about 70% of the tested websites after two months. We conclude by discussing strategies for website owners and hosting providers towards hindering IP-based website fingerprinting and maximizing the privacy benefits offered by DoT/DoH and ECH.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Feb 2021.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 0; 2026: 1.
3 papers.
- 2022Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantificationcited by 9
- 2023Augmenting Rule-based DNS Censorship Detection at Scale with Machine Learningcited by 1
- 2026CipherSight: Robust Website Fingerprinting via Record-Resource Semantic Supervision under Distribution Shiftscited by 1
- 2026CipherSight: Robust Website Fingerprinting via Record-Resource Semantic Supervision under Distribution Shifts14 Aug 2026
- 2023Augmenting Rule-based DNS Censorship Detection at Scale with Machine Learning3 Feb 2023
- 2022Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification11 May 2022