Pascal Paillier

TL;DR: This work identifies and fills some gaps with regard to consistency (the extent to which false positives are produced) for public-key encryption with keyword search (PEKS) and defines computational and statistical relaxations of the existing notion of perfect consistency.

TL;DR: This work identifies and fills some gaps with regard to consistency (the extent to which false positives are produced) for public-key encryption with keyword search (PEKS) and provides a transform of an anonymous IBE scheme to a secure PEKS scheme that guarantees consistency.

TL;DR: The rise of machine learning as a service multiplies scenarios where one faces a privacy dilemma: either sensitive user data must be revealed to the entity that evaluates the cognitive model, or the model itself must be reveal to the user so that the evaluation can take place locally.

TL;DR: New efficient constructions for public-key broadcast encryption that simultaneously enjoy the following properties are put forward: receivers are stateless; encryption is collusion-secure for arbitrarily large collusions of users and security is tight in the standard model.