55 citations · 220 across the 35 of their papers we have counts for
6 papers · 1 filter
Towards Robust Dataset Learning
Yihan Wu, Xinda Li, Florian Kerschbaum +2
Adversarial training has been actively studied in recent computer vision research to improve the robustness of models. However, due to the huge computational cost of generating adv…
Comprehension from Chaos: Towards Informed Consent for Private Computation
Bailey Kacsmar, Vasisht Duddu, Kyle Tilbury +2
Private computation, which includes techniques like multi-party computation and private query execution, holds great promise for enabling organizations to analyze data they and the…
Faster Secure Comparisons with Offline Phase for Efficient Private Set Intersection
Florian Kerschbaum, Erik-Oliver Blass, Rasoul Akhavan Mahdavi
In a Private section intersection (PSI) protocol, Alice and Bob compute the intersection of their respective sets without disclosing any element not in the intersection. PSI protoc…
The Limits of Word Level Differential Privacy
Justus Mattern, Benjamin Weggenmann, Florian Kerschbaum
As the issues of privacy and trust are receiving increasing attention within the research community, various attempts have been made to anonymize textual data. A significant subset…
Assessing Differentially Private Variational Autoencoders under Membership Inference
Daniel Bernau, Jonas Robl, Florian Kerschbaum
We present an approach to quantify and compare the privacy-accuracy trade-off for differentially private Variational Autoencoders. Our work complements previous work in two aspects…
Constant-weight PIR: Single-round Keyword PIR via Constant-weight Equality Operators
Rasoul Akhavan Mahdavi, Florian Kerschbaum
Equality operators are an essential building block in tasks over secure computation such as private information retrieval. In private information retrieval (PIR), a user queries a…