Machine learning (ML) is increasingly being adopted in a wide variety of application
domains. Usually, a well-performing ML model relies on a large volume of training data and …

The wide adoption of Machine Learning to solve a large set of real-life problems came with
the need to collect and process large volumes of data, some of which are considered …

Federated learning (FL) has been proposed to allow collaborative training of machine
learning (ML) models among multiple parties to keep their data private and only model …

We construct new functional encryption schemes that combine the access control
functionality of attribute-based encryption with the possibility of performing linear operations …

We propose Agora, the first blockchain-based data marketplace that enables multiple
privacy-concerned parties to get compensated for contributing and exchanging data, without …

We present a new generic construction of multi-client functional encryption (MCFE) for inner
products from single-input functional inner-product encryption and standard pseudorandom …

Decentralized multi-client functional encryption for inner product (DMCFE-IP) enables
efficient joint functional computation of private inputs in a secure manner without a trusted …

Multi-input functional encryption, MIFE, is a powerful generalization of functional encryption
that allows computation on encrypted data coming from multiple different data sources. In a …

Abstract We introduce Dynamic Decentralized Functional Encryption (DDFE), a
generalization of Functional Encryption which allows multiple users to join the system …

Multi-client functional encryption (MCFE) allows ℓ clients to encrypt ciphertexts (C _ t, 1, C _
t, 2, ..., C _ t, ℓ) under some label. Each client can encrypt his own data X_i for a label t using …