Hiding the Access Pattern is Not Enough: Exploiting Search Pattern Leakage in Searchable Encryption
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. These schemes focus...
IHOP: Improved Statistical Query Recovery against Searchable Symmetric Encryption through Quadratic Optimization
Effective query recovery attacks against Searchable Symmetric Encryption (SSE) schemes typically rely on auxiliary ground-truth information about the queries or dataset. Query recovery is also...
Obfuscated Access and Search Patterns in Searchable Encryption
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 matched documents....
Investigating Membership Inference Attacks under Data Dependencies
Training machine learning models on privacy-sensitive data has become a popular practice, driving innovation in ever-expanding fields. This has opened the door to new attacks that can have serious...
Generalization Techniques Empirically Outperform Differential Privacy against Membership Inference
Differentially private training algorithms provide protection against one of the most popular attacks in machine learning: the membership inference attack. However, these privacy algorithms incur...
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 outgoing messages, are the main building block of high-latency anonymous communication networks. A number of so-called...
Meet the Family of Statistical Disclosure Attacks
Disclosure attacks aim at revealing communication patterns in anonymous communication systems, such as conversation partners or frequency. In this paper, we propose a framework to compare between...
Back to the Drawing Board: Revisiting the Design of Optimal Location Privacy-preserving Mechanisms
In the last years we have witnessed the appearance of a variety of strategies to design optimal location privacy-preserving mechanisms, in terms of maximizing the adversary's expected error with...
Rethinking Location Privacy for Unknown Mobility Behaviors
Location Privacy-Preserving Mechanisms (LPPMs) in the literature largely consider that users' data available for training wholly characterizes their mobility patterns. Thus, they hardwire this...
Is Geo-Indistinguishability What You Are Looking for?
Since its proposal in 2013, geo-indistinguishability has been consolidated as a formal notion of location privacy, generating a rich body of literature building on this idea. A problem with most...
Understanding the Effects of Real-World Behavior in Statistical Disclosure Attacks
High-latency anonymous communication systems prevent passive eavesdroppers from inferring communicating partners with certainty. However, disclosure attacks allow an adversary to recover users'...