Privacy-Preserving Federated Learning (PPFL) is an emerging secure distributed learning
paradigm that aggregates user-trained local gradients into a federated model through a …

Several recently proposed ciphers, for example Rijndael and Serpent, are built with layers of
small S-boxes interconnected by linear key-dependent layers. Their security relies on the …

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 …

To be able to develop and test robust affective multimodal systems, researchers need
access to novel databases containing representative samples of human multi-modal …

We propose a generalization of Paillier's probabilistic public-key system, in which the
expansion factor is reduced and which allows to adjust the block length of the scheme even …

Signal processing tools working directly on encrypted data could provide an efficient
solution to application scenarios where sensitive signals must be protected from an …

Decentralized optimization is crucial for the design, deployment, and functionality of many
distributed systems. In this paper, we address the problem of privacy-preservation in …

We present an efficient construction of Yao's “garbled circuits” protocol for securely
computing any two-party circuit on committed inputs. The protocol is secure in a universally …

This paper is concerned with generalisations of Paillier's probabilistic encryption scheme
from the integers modulo a square to elliptic curves over rings. Paillier himself described two …

Signal processing in the encrypted domain (sped) appears an elegant solution in
application scenarios, where valuable signals must be protected from a possibly malicious …