Alexander Viand

I graduated from ETH Zurich in May 2023 and am now continuing similar research at Intel Labs. Before that, I was a doctoral student & research assistant in the Applied Cryptography Group at ETH Zürich and a member of the Privacy Preserving Systems Lab. I also received both my MSc and BSc in Computer Science from ETH Zürich. During my PhD, I had the opportunity to be a visiting scholar with Tobias Grosser at the University of Edinburgh and with Dawn Song at UC Berkeley.

My interests include useable security and privacy, privacy enhancing technologies, and the interactions between these technologies and society. In my research, I work with secure computation technologies including Fully Homomorphic Encryption, Secure Multi-Party Computation and Zero-Knowledge Proofs, trying to make these techniques more accessible to non-experts by developing new systems, tools and abstractions.

I am looking for motivated students who are interested in conducting (potentially industry-based) student thesis or projects related to my research areas. In addition to the projects listed here, you are also very welcome to send me an email to discuss further details or additional project possibilities.

Talks:

FHE Development Ecosystem: Tools, Compilers & Challenges.

• Stanford, Security Seminar
• UC Berkeley, NetSys Seminar
• Intel Labs
• Nvidia, FHE - Research Team
• FHE.org Meetup, [Video,Slides]

HECO: Automatic Code Optimizations for Efficient Fully Homomorphic Encryption.

• FHE.org conference, Trondheim, Norway [Slides]
• Protocol Labs, Research Seminar [Video]
• SRC Techcon

Building an End-to-End Toolchain for Fully Homomorphic Encryption with MLIR.

• LLVM Developer Meeting

Usable FHE: Opportunities & Challenges.

• UC Berkeley, Security Seminar
• UC San Diego
• Stanford, Security Seminar

Selected 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 Github 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)

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.

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

Pyfhel: PYthon For Homomorphic Encryption Libraries Paper Slides Github

Alberto Ibarrondo, Alexander Viand

Workshop on Encrypted Computing & Applied Homomorphic Cryptography (WAHC '21).

Private Outsourced Translation for Medical Data. Paper Github

Travis Morrison, Bijeeta Pal, Sarah Scheffler, Alexander Viand

In "Protecting Privacy through Homomorphic Encryption" K. Lauter, W. Dai, and K. Laine, editors. Springer, 2021.

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.

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.

Robust Secure Aggregation for Privacy-Preserving Federated Learning with Adversaries Paper

Lukas Burkhalter, Alexander Viand, Matthias Lei, Hossein Shafagh, Anwar Hithnawi

Privacy Preserving Machine Learning Workshop (PPML), 2019.

Marble: Making Fully Homomorphic Encryption Accessible to All. Paper Github

Alexander Viand, Hossein Shafagh

Workshop on Encrypted Computing & Applied Homomorphic Cryptography (WAHC '18). Toronto, Canada,