100 citations · 138 across the 13 of their papers we have counts for
13 papers
Atlas: Efficient Verifiable Semantic Search
Nikolay Avramov, Hidde Lycklama, Alexander Viand +1
Semantic search is a core primitive of modern applications, powering recommender systems, web search, and retrieval-augmented generation for language models. The provider controls…
Enhancing Privacy, Neglecting Harms: An Analysis of Real-World Digital Privacy Incidents
Shannon Veitch, C. Shem, Lena Csomor +7
Privacy-enhancing technologies (PETs) have emerged as a technical means for providing individuals with greater control over their information. Yet despite the growing deployment of…
HEIR: A Universal Compiler for Homomorphic Encryption
Asra Ali, Jaeho Choi, Bryant Gipson +7
This work presents Homomorphic Encryption Intermediate Representation (HEIR), a unified approach to building homomorphic encryption (HE) compilers. HEIR aims to support all mainstr…
DPolicy: Managing Privacy Risks Across Multiple Releases with Differential Privacy
Nicolas Küchler, Alexander Viand, Hidde Lycklama +1
Differential Privacy (DP) has emerged as a robust framework for privacy-preserving data releases and has been successfully applied in high-profile cases, such as the 2020 US Census…
Artemis: Efficient Commit-and-Prove SNARKs for zkML
Hidde Lycklama, Alexander Viand, Nikolay Avramov +2
Ensuring that AI models are both verifiable and privacy-preserving is important for trust, accountability, and compliance. To address these concerns, recent research has focused on…
Holding Secrets Accountable: Auditing Privacy-Preserving Machine Learning
Hidde Lycklama, Alexander Viand, Nicolas Küchler +2
Recent advancements in privacy-preserving machine learning are paving the way to extend the benefits of ML to highly sensitive data that, until now, have been hard to utilize due t…
Verifiable Fully Homomorphic Encryption
Alexander Viand, Christian Knabenhans, Anwar Hithnawi
Fully Homomorphic Encryption (FHE) is seeing increasing real-world deployment to protect data in use by allowing computation over encrypted data. However, the same malleability tha…
Cohere: Managing Differential Privacy in Large Scale Systems
Nicolas Küchler, Emanuel Opel, Hidde Lycklama +2
The need for a privacy management layer in today's systems started to manifest with the emergence of new systems for privacy-preserving analytics and privacy compliance. As a resul…
HECO: Fully Homomorphic Encryption Compiler
Alexander Viand, Patrick Jattke, Miro Haller +1
In recent years, Fully Homomorphic Encryption (FHE) has undergone several breakthroughs and advancements, leading to a leap in performance. Today, performance is no longer a major…
Zeph: Cryptographic Enforcement of End-to-End Data Privacy
Lukas Burkhalter, Nicolas Küchler, Alexander Viand +2
As increasingly more sensitive data is being collected to gain valuable insights, the need to natively integrate privacy controls in data analytics frameworks is growing in importa…
RoFL: Robustness of Secure Federated Learning
Hidde Lycklama, Lukas Burkhalter, Alexander Viand +2
Even though recent years have seen many attacks exposing severe vulnerabilities in Federated Learning (FL), a holistic understanding of what enables these attacks and how they can…
SoK: Fully Homomorphic Encryption Compilers
Alexander Viand, Patrick Jattke, Anwar Hithnawi
Fully Homomorphic Encryption (FHE) allows a third party to perform arbitrary computations on encrypted data, learning neither the inputs nor the computation results. Hence, it prov…
TimeCrypt: Encrypted Data Stream Processing at Scale with Cryptographic Access Control
Lukas Burkhalter, Anwar Hithnawi, Alexander Viand +2
A growing number of devices and services collect detailed time series data that is stored in the cloud. Protecting the confidentiality of this vast and continuously generated data…