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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

Secure and Robust Collaborative Learning thumbnail

Secure and Robust Collaborative Learning

End-to-End Designs for Data Privacy thumbnail

End-to-End Designs for Data Privacy

Accessible Privacy Preserving Computation thumbnail

Accessible Privacy Preserving Computation

Privacy Preserving Stream Analytics at Scale thumbnail

Privacy Preserving Stream Analytics at Scale

News

Arc is accepted at USENIX Security 2024!

Cohere is accepted at IEEE S&P (Oakland) 2024!

We received the best talk award at HotPETs for our work on "Bridging the Gap between Privacy Incidents and PETs"!

Alex successfully defended his PhD thesis last week. Congratulations, Dr. Alex! Photo.

RoFL is accepted at IEEE S&P (Oakland) 2023!

Chris gave talks about Verifiable Fully Homomorphic Encryption at UC Berkeley Sec. Seminar, Stanford Sec. Seminar, Intel Labs, UCSD (invited guest lecture), and the FHE.org conference. Check the talk here.

We received the Google Research Award!

This award will foster a new collaboration with Google that aims to bring efficient and scheme-agnostic FHE support to the TensorFlow framework.

HECO is accepted at USENIX Security 2023!

Congratulations to Lukas Burkhalter, who successfully defended his Ph.D. thesis this week! Photo.

We have a new group photo. Here is a photo of us!

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Publications

Thumbnail of Artemis: Efficient Commit-and-Prove SNARKs for zkML

Artemis: Efficient Commit-and-Prove SNARKs for zkML Paper Github

Hidde Lycklama*, Alexander Viand*, Nikolay Avramov, Nicolas Küchler, Anwar Hithnawi

Preprint, arXiv:2409.12055

Thumbnail of Holding Secrets Accountable: Auditing Privacy-Preserving Machine Learning

Holding Secrets Accountable: Auditing Privacy-Preserving Machine Learning Paper Slides Github

Hidde Lycklama, Alexander Viand, Nicolas Küchler, Christian Knabenhans, Anwar Hithnawi

USENIX Security 2024.

Thumbnail of Cohere: Managing Differential Privacy in Large Scale Systems

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.

Thumbnail of vFHE: Verifiable Fully Homomorphic Encryption

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)

Thumbnail of CoVault: Secure Selective Analytics of Sensitive Data for the Public Good.

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

Thumbnail of RoFL: Robustness of Secure Federated Learning

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.

Thumbnail of HECO: Fully Homomorphic Encryption Compiler.

HECO: Fully Homomorphic Encryption Compiler. Paper Github Video

Alexander Viand, Patrick Jattke, Miro Haller, Anwar Hithnawi

USENIX Security 2023.

Thumbnail of VF-PS: How to Select Important Participants in Vertical Federated Learning, Efficiently and Securely?.

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.

Thumbnail of Cryptographic Auditing for Collaborative Learning

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

Thumbnail of Zeph: Cryptographic Enforcement of End-to-End Data Privacy.

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.

Thumbnail of SoK: Fully Homomorphic Encryption Compilers.

SoK: Fully Homomorphic Encryption Compilers. Paper Slides Github Website Video

Alexander Viand, Patrick Jattke, Anwar Hithnawi

IEEE Security and Privacy (Oakland) 2021.

Thumbnail of Droplet: Decentralized Authorization and Access Control for Encrypted Data Streams.

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.

Thumbnail of TimeCrypt: Encrypted Data Stream Processing at Scale with Cryptographic Access Control.

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.

Research Highlights

Verifiable Fully Homomorphic Encryption
Security and Robustness of Collaborative Learning
FHE Development Ecosystem: Tools, Compilers & Challenges
HECO: Fully Homomorphic Encryption Compiler
Zeph: Cryptographic Enforcement of Privacy
Towards Robust FHE for the Real World
Cohere Managing Differential Privacy in Large Scale Systems
RoFL: Robustness of Secure Federated Learning