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Alexander Viand

I am a doctoral student & research assistant in the Applied Cryptography Group at ETH Zürich and a member of the Privacy Preserving Systems Lab. I've received both my MSc and BSc in Computer Science from ETH Zürich.

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.

We are always looking for motivated students who are interested in conducting 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.

Teaching:

  • Informatik II für D-ITET (2019-2021)
  • Theoretische Informatik (2013-2018)

Selected Publications:

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

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

Lukas Burkhalter*, Nicolas Küchler*, Alexander Viand, Hossein Shafagh, Anwar Hithnawi

USENIX OSDI 2021. Online.

Thumbnail of SoK: Fully Homomorphic Encryption Compilers.

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

Alexander Viand, Patrick Jattke, Anwar Hithnawi

IEEE Symposium on Security and Privacy 2021. Online.

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. Santa Clara, California, USA.

Thumbnail of Robust Secure Aggregation for Privacy-Preserving Federated Learning with Adversaries

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.

Thumbnail of Marble: Making Fully Homomorphic Encryption Accessible to All.

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,