15 citations · 64 across the 14 of their papers we have counts for
14 papers
PEPSI: Practically Efficient Private Set Intersection in the Unbalanced Setting
Rasoul Akhavan Mahdavi, Nils Lukas, Faezeh Ebrahimianghazani +7
Two parties with private data sets can find shared elements using a Private Set Intersection (PSI) protocol without revealing any information beyond the intersection. Circuit PSI p…
Fast and Private Inference of Deep Neural Networks by Co-designing Activation Functions
Abdulrahman Diaa, Lucas Fenaux, Thomas Humphries +9
Machine Learning as a Service (MLaaS) is an increasingly popular design where a company with abundant computing resources trains a deep neural network and offers query access for t…
Generalization Techniques Empirically Outperform Differential Privacy against Membership Inference
Jiaxiang Liu, Simon Oya, Florian Kerschbaum
Differentially private training algorithms provide protection against one of the most popular attacks in machine learning: the membership inference attack. However, these privacy a…
IHOP: Improved Statistical Query Recovery against Searchable Symmetric Encryption through Quadratic Optimization
Simon Oya, Florian Kerschbaum
Effective query recovery attacks against Searchable Symmetric Encryption (SSE) schemes typically rely on auxiliary ground-truth information about the queries or dataset. Query reco…
Obfuscated Access and Search Patterns in Searchable Encryption
Zhiwei Shang, Simon Oya, Andreas Peter +1
Searchable Symmetric Encryption (SSE) allows a data owner to securely outsource its encrypted data to a cloud server while maintaining the ability to search over it and retrieve ma…
Hiding the Access Pattern is Not Enough: Exploiting Search Pattern Leakage in Searchable Encryption
Simon Oya, Florian Kerschbaum
Recent Searchable Symmetric Encryption (SSE) schemes enable secure searching over an encrypted database stored in a server while limiting the information leaked to the server. Thes…