Liang Wang, Hyojoon Kim, Prateek Mittal, Jennifer Rexford (Princeton University)

In conventional DNS, or Do53, requests and responses are sent in cleartext. Thus, DNS recursive resolvers or any on-path adversaries can access privacy-sensitive information. To address this issue, several encryption-based approaches (e.g., DNS-over-HTTPS) and proxy-based approaches (e.g., Oblivious DNS) were proposed. However, encryption-based approaches put too much trust in recursive resolvers. Proxy-based approaches can help hide the client’s identity, but sets a higher deployment barrier while also introducing noticeable performance overhead. We propose PINOT, a packet-header obfuscation system that runs entirely in the data plane of a programmable network switch, which provides a lightweight, low-deployment-barrier anonymization service for clients sending and receiving DNS packets. PINOT does not require any modification to the DNS protocol or additional client software installation or proxy setup. Yet, it can also be combined with existing approaches to provide stronger privacy guarantees. We implement a PINOT prototype on a commodity switch, deploy it in a campus network, and present results on protecting user identity against public DNS services.

View More Papers

Let’s Stride Blindfolded in a Forest: Sublinear Multi-Client Decision...

Jack P. K. Ma (The Chinese University of Hong Kong), Raymond K. H. Tai (The Chinese University of Hong Kong), Yongjun Zhao (Nanyang Technological University), Sherman S.M. Chow (The Chinese University of Hong Kong)

Read More

When DNS Goes Dark: Understanding Privacy and Shaping Policy...

Vijay k. Gurbani and Cynthia Hood ( Illinois Institute of Technology), Anita Nikolich (University of Illinois), Henning Schulzrinne (Columbia University) and Radu State (University of Luxembourg)

Read More

IoTSafe: Enforcing Safety and Security Policy with Real IoT...

Wenbo Ding (Clemson University), Hongxin Hu (University at Buffalo), Long Cheng (Clemson University)

Read More

POSEIDON: Privacy-Preserving Federated Neural Network Learning

Sinem Sav (EPFL), Apostolos Pyrgelis (EPFL), Juan Ramón Troncoso-Pastoriza (EPFL), David Froelicher (EPFL), Jean-Philippe Bossuat (EPFL), Joao Sa Sousa (EPFL), Jean-Pierre Hubaux (EPFL)

Read More