Christian Knabenhans
Research Assistant @ Privacy-Preserving Systems Lab at ETH Zurich, incoming doctoral student at EPFL
PPS Lab
About me
I’m a research assistant working at the intersection of applied cryptography and systems at the Privacy-Preserving Systems Lab at ETH Zurich 🇨🇭, and I’ll be starting my Ph.D. at EPFL in September.
At the moment, I’m working on making Fully Homorphic Encryption robust in malicious settings, in particular by using novel zero-knowledge proofs.
In the past, I’ve been dabbling in static analysis and formal verification and privacy issues of machine learning. In my free time, I fence and rant about opera. I’ve also been involved in the non-profits ETH Cyber Group — Student Initiative and L’Association Francophone des Étudiants de Zurich.
Recent News
Publications
(2023).
Verifiable Fully Homomorphic Encryption.
under submission.
(2022).
Verifiable Encodings for Secure Homomorphic Analytics.
under submission.
Projects
Experience
Research Assistant
Investigating integrity protection for modern FHE use cases, by analyzing, improving and implementing zero-knowledge proof systems for FHE.
President, Board Member for Communication and Marketing
As a student association at ETH Zurich, Cyber Group brings the topic of cyber security closer to students from all departments, and aims to create a network between students, academia, industry, and the public sector.
Cyber Group trains students teams for Cyber 9/12 Strategy Challenges, and organizes bi-monthly Open Port talks, as well as its yearly Meet & Greet networking and conference event.
Cyber Group trains students teams for Cyber 9/12 Strategy Challenges, and organizes bi-monthly Open Port talks, as well as its yearly Meet & Greet networking and conference event.
Student Research Assistant
Summer@EPFL Intern
Research internship supported by a fellowship grant
Enabling verifiable computations for fully homomorphic encryption systems.
Implementation of a new theoretical framework using the open-source Lattigo library.
Enabling verifiable computations for fully homomorphic encryption systems.
Implementation of a new theoretical framework using the open-source Lattigo library.
Student Research Assistant
Developed and implemented a framework to predict and quantify property inference attacks in deep machine learning models (particularly in the context of federated learning), using measures of distance between machine learning tasks (derived using transfer learning)
Security Intern
Searched for vulnerabilities in the Airlock Web Application Firewall filter rules using automated fuzzing
Patched, extended and optimized filter rules for XSS, SQLi, and multipart attacks
Helped set up a Bug Bounty program
Patched, extended and optimized filter rules for XSS, SQLi, and multipart attacks
Helped set up a Bug Bounty program