Abstract Upon the General Data Protection Regulation's (GDPR) application on 25 May
2018 across the European Union, new legal requirements for the protection of personal data …

Speech recordings are a rich source of personal, sensitive data that can be used to support
a plethora of diverse applications, from health profiling to biometric recognition. It is therefore …

Secure Multi-party Computation (MPC) allows a set of mutually distrusting parties to jointly
evaluate a function on their private inputs while maintaining input privacy. In this work, we …

We present Chameleon, a novel hybrid (mixed-protocol) framework for secure function
evaluation (SFE) which enables two parties to jointly compute a function without disclosing …

Secure computation enables mutually distrusting parties to jointly evaluate a function on
their private inputs without revealing anything but the function's output. Generic secure …

We introduce hand movement, orientation, and grasp (HMOG), a set of behavioral features
to continuously authenticate smartphone users. HMOG features unobtrusively capture subtle …

In biometric identification systems, the biometric database is typically stored in a trusted
server, which is also responsible for performing the identification process. However, a …

In spite of the advantages of biometrics as an identity verification technology, some concerns
have been raised due to the high sensitivity of biometric data: any information leakage …

Performing machine learning (ML) computation on private data while maintaining data
privacy, aka Privacy-preserving Machine Learning (PPML), is an emergent field of research …

We present EzPC, a secure two-party computation (2PC) framework that generates efficient
2PC protocols from high-level, easy-to-write programs. EzPC provides formal correctness …