This repo has been archived and is only available to run the demo.
Drynx is a decentralized system that can perform statistical analysis and machine learning on sensitive and distributed data while ensuring data confidentiality and privacy. It uses interactive protocols, homomorphic encryption, zero-knowledge proofs of correctness, and differential privacy to enable an efficient and decentralized verification of the input data and of all the system’s computations. Drynx is modular, dynamic, and parallelizable. The evaluation shows that Drynx can train a logistic regression model on a dataset (12 features and 600,000 records) distributed among 12 data providers in less than 2 seconds, and the verification of the query execution’s correctness takes less than 22 seconds.
You can find more information in the C4DT showcase entry.
To run the demo, run
docker-compose up
The docker images are available for linux/amd64 only, but using Rosetta makes them also work on mac with M1/M2
processors.
WARNING: The nodes in this setup are using hardcoded public/private keypairs from the nodes folder, do not deploy this setup in production! You can generate new config files with:
sh generate-config.sh
But then you also need to update the public keys in webapp/src/app/config.service.ts.
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.