Privacy Preserving Systems Lab
We at the PPS Lab build, investigate, and research software systems for data privacy and data security. Our mission is to develop technologies that enable applications to safely and securely interact with users data while preserving individual's privacy and make it easy for developers to build and develop privacy preserving applications.
Projects
News
This award will foster a new collaboration with Google that aims to bring efficient and scheme-agnostic FHE support to the TensorFlow framework.
This grant aims at expanding our efforts to build an ecosystem for developing and deploying FHE applications.
Check the talk here and an extended version of the talk, ending with an interesting discussion on FHE tools development here.
This grant aims at expanding our efforts on end-to-end systems designs for data privacy.
Anwar Hithnawi got awarded the SNSF Ambizione grant to start the PPS Lab at ETH Zurich.
Publications
Artemis: Efficient Commit-and-Prove SNARKs for zkML Paper Github
Hidde Lycklama*, Alexander Viand*, Nikolay Avramov, Nicolas Küchler, Anwar Hithnawi
Preprint, arXiv:2409.12055
Holding Secrets Accountable: Auditing Privacy-Preserving Machine Learning Paper Slides Github Video
Hidde Lycklama, Alexander Viand, Nicolas Küchler, Christian Knabenhans, Anwar Hithnawi
USENIX Security 2024.
Cohere: Managing Differential Privacy in Large Scale Systems Paper Slides Video
Nicolas Küchler, Emanuel Opel, Hidde Lycklama, Alexander Viand, Anwar Hithnawi
IEEE Security and Privacy (Oakland) 2024.
vFHE: Verifiable Fully Homomorphic Encryption Paper Github Video
Christian Knabenhans*, Alexander Viand*, Antonio Merino-Gallardo, Anwar Hithnawi
Workshop on Encrypted Computing & Applied Homomorphic Cryptography (WAHC '24)
CoVault: Secure Selective Analytics of Sensitive Data for the Public Good. Paper
Roberta De Viti, Isaac Sheff, Noemi Glaeser, Baltasar Dinis, Rodrigo Rodrigues, Jonathan Katz, Bobby Bhattacharjee, Anwar Hithnawi, Deepak Garg, Peter Druschel
Preprint, arXiv:2301.08517
RoFL: Robustness of Secure Federated Learning Paper Slides Github Video
Hidde Lycklama*, Lukas Burkhalter*, Alexander Viand, Nicolas Küchler, Anwar Hithnawi
IEEE Security and Privacy (Oakland) 2023.
HECO: Fully Homomorphic Encryption Compiler. Paper Github Video
Alexander Viand, Patrick Jattke, Miro Haller, Anwar Hithnawi
USENIX Security 2023.
VF-PS: How to Select Important Participants in Vertical Federated Learning, Efficiently and Securely?. Paper
Jiawei Jiang, Lukas Burkhalter, Fangcheng Fu, Bolin Ding, Bo Du, Anwar Hithnawi, Bo Li, Ce Zhang
NeurIPS (Spotlight) 2022.
Cryptographic Auditing for Collaborative Learning Paper Poster
Hidde Lycklama, Nicolas Küchler, Alexander Viand, Emanuel Opel, Lukas Burkhalter, Anwar Hithnawi
ML Safety Workshop at NeurIPS 2022
Zeph: Cryptographic Enforcement of End-to-End Data Privacy. Paper Slides Github Video
Lukas Burkhalter*, Nicolas Küchler*, Alexander Viand, Hossein Shafagh, Anwar Hithnawi
USENIX OSDI 2021.
SoK: Fully Homomorphic Encryption Compilers. Paper Slides Github Website Video
Alexander Viand, Patrick Jattke, Anwar Hithnawi
IEEE Security and Privacy (Oakland) 2021.
Droplet: Decentralized Authorization and Access Control for Encrypted Data Streams. Paper Slides Github Website Video
Hossein Shafagh, Lukas Burkhalter, Sylvia Ratnasamy, Anwar Hithnawi
USENIX Security 2020.
TimeCrypt: Encrypted Data Stream Processing at Scale with Cryptographic Access Control. Paper Slides Github Website Video
Lukas Burkhalter, Anwar Hithnawi, Alexander Viand, Hossein Shafagh, Sylvia Ratnasamy
USENIX NSDI 2020.