research
∙
06/14/2023
Fast and Private Inference of Deep Neural Networks by Co-designing Activation Functions
Machine Learning as a Service (MLaaS) is an increasingly popular design ...
share
research
∙
10/11/2021
Generalization Techniques Empirically Outperform Differential Privacy against Membership Inference
Differentially private training algorithms provide protection against on...
research
∙
10/08/2021
IHOP: Improved Statistical Query Recovery against Searchable Symmetric Encryption through Quadratic Optimization
Searchable Symmetric Encryption (SSE) schemes allow a client to perform ...
research
∙
02/18/2021
Obfuscated Access and Search Patterns in Searchable Encryption
Searchable Symmetric Encryption (SSE) allows a data owner to securely ou...
research
∙
10/23/2020
Differentially Private Learning Does Not Bound Membership Inference
Training machine learning models on privacy-sensitive data has become a ...
research
∙
10/07/2020
Hiding the Access Pattern is Not Enough: Exploiting Search Pattern Leakage in Searchable Encryption
Recent Searchable Symmetric Encryption (SSE) schemes enable secure searc...
research
∙
10/22/2019
Filter Design for Delay-Based Anonymous Communications
In this work, we address the problem of designing delay-based anonymous ...
research
∙
10/22/2019
Understanding the Effects of Real-World Behavior in Statistical Disclosure Attacks
High-latency anonymous communication systems prevent passive eavesdroppe...
research
∙
10/17/2019
A Least Squares Approach to the Static Traffic Analysis of High-Latency Anonymous Communication Systems
Mixes, relaying routers that hide the relation between incoming and outg...
research
∙
10/16/2019
Meet the Family of Statistical Disclosure Attacks
Disclosure attacks aim at revealing communication patterns in anonymous ...
research
∙
09/12/2018