P
Pascal Paillier
Researcher at Gemalto
Publications - 122
Citations - 11072
Pascal Paillier is an academic researcher from Gemalto. The author has contributed to research in topics: Encryption & Public-key cryptography. The author has an hindex of 30, co-authored 122 publications receiving 9715 citations. Previous affiliations of Pascal Paillier include MIPS Technologies.
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Book ChapterDOI
Public-key cryptosystems based on composite degree residuosity classes
TL;DR: A new trapdoor mechanism is proposed and three encryption schemes are derived : a trapdoor permutation and two homomorphic probabilistic encryption schemes computationally comparable to RSA, which are provably secure under appropriate assumptions in the standard model.
Book ChapterDOI
Searchable encryption revisited: consistency properties, relation to anonymous IBE, and extensions
Michel Abdalla,Mihir Bellare,Dario Catalano,Eike Kiltz,Tadayoshi Kohno,Tanja Lange,John Malone-Lee,Gregory Neven,Pascal Paillier,Haixia Shi +9 more
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.
Journal ArticleDOI
Searchable Encryption Revisited: Consistency Properties, Relation to Anonymous IBE, and Extensions
Michel Abdalla,Mihir Bellare,Dario Catalano,Eike Kiltz,Tadayoshi Kohno,Tanja Lange,John Malone-Lee,Gregory Neven,Pascal Paillier,Haixia Shi +9 more
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.
Book ChapterDOI
Fully collusion secure dynamic broadcast encryption with constant-size ciphertexts or decryption keys
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.
Book ChapterDOI
Fast Homomorphic Evaluation of Deep Discretized Neural Networks
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.