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Data sharing has become of primary importance in many domains such asbig-data analytics, economics and medical research, but remains difficult toachieve when the data are sensitive. In fact, sharing personal informationrequires individuals' unconditional consent or is often simply forbidden forprivacy and security reasons. In this paper, we propose Drynx, a decentralizedsystem for privacy-conscious statistical analysis on distributed datasets.Drynx relies on a set of computing nodes to enable the computation ofstatistics such as standard deviation or extrema, and the training andevaluation of machine-learning models on sensitive and distributed data. Toensure data confidentiality and the privacy of the data providers, Drynxcombines interactive protocols, homomorphic encryption, zero-knowledge proofsof correctness, and differential privacy. It enables an efficient anddecentralized verification of the input data and of all the system'scomputations thus provides auditability in a strong adversarial model in whichno entity has to be individually trusted. Drynx is highly modular, dynamic andparallelizable. Our evaluation shows that it enables the training of a logisticregression model on a dataset (12 features and 600,000 records) distributedamong 12 data providers in less than 2 seconds. The computations aredistributed among 6 computing nodes, and Drynx enables the verification of thequery execution's correctness in less than 22 seconds.

Drynx: Decentralized, Secure, Verifiable System for Statistical Queries and Machine Learning on Distributed Datasets