  Data sharing has become of primary importance in many domains such as
big-data analytics, economics and medical research, but remains difficult to
achieve when the data are sensitive. In fact, sharing personal information
requires individuals' unconditional consent or is often simply forbidden for
privacy and security reasons. In this paper, we propose Drynx, a decentralized
system for privacy-conscious statistical analysis on distributed datasets.
Drynx relies on a set of computing nodes to enable the computation of
statistics such as standard deviation or extrema, and the training and
evaluation of machine-learning models on sensitive and distributed data. To
ensure data confidentiality and the privacy of the data providers, Drynx
combines interactive protocols, homomorphic encryption, zero-knowledge proofs
of correctness, and differential privacy. It enables an efficient and
decentralized verification of the input data and of all the system's
computations thus provides auditability in a strong adversarial model in which
no entity has to be individually trusted. Drynx is highly modular, dynamic and
parallelizable. Our evaluation shows that it enables the training of a logistic
regression model on a dataset (12 features and 600,000 records) distributed
among 12 data providers in less than 2 seconds. The computations are
distributed among 6 computing nodes, and Drynx enables the verification of the
query execution's correctness in less than 22 seconds.
