C4DT Incubator

These are the current projects that the C4DT team is working on. You find code and references to the papers.

Drynx

Drynx allows to create privacy-preserving queries on encrypted datasets that are stored at different data providers who don't want to share the original data. Different types of statistical queries are possible, like average, standard deviation, linear and logistic regressions - all using homomorphic encryption, which means that the data is never shared in cleartext.
If you are interested, please contact: Valérian Rousset

Lattigo

Homomorphic encryption refers to cryptographic schemes that allow to compute directly on the encrypted data, without requiring the data to be decrypted first. It therefore enables a set of operations (polynomial evaluation and approximations of non-polynomial functions) to be "blindly" performed by an untrustworthy party on encrypted sensitive data, with a controlled computational overhead. This technology is very promising as an enabler for secure data sharing, and in particular, in the case of secure multi-party computations, where several entities want to collaboratively compute a function on their confidential or sensitive data, without revealing to each other anything else than the result of this computation.
If you are interested, please contact: Christian Grigis

MLBench

Framework for distributed machine learning. Its purpose is to improve transparency, reproducibility, robustness, and to provide fair performance measures as well as reference implementations, helping adoption of distributed machine learning methods both in industry and in the academic community. Besides algorithm comparison, a main use case is to help the selection of hardware (CPU, GPU) used to run AI applications, as well as how to connect it into a cluster to get a good cost/performance tradeoff.
If you are interested, please contact: Christian Grigis

MedCo

MedCo enables privacy-preserving cohort exploration by providing ways of querying medical datasets with end-to-end encryption of queries, data at rest, in transfer and during computation, all the while guaranteeing differential privacy.
If you are interested, please contact: Christian Grigis

OmniLedger

OmniLedger is a high performance blockchain solution developed by the DEDIS-lab. It supports pre-compiled smart contracts, as well as Ethereum smart contracts. Combined with Calypso, it allows management of access to secure and private data in a fully audited way.
If you are interested, please contact: Linus Gasser

SPINDLE

SPINDLE allows to train and evaluate generalized linear models on datasets that are stored at different data providers who don't want to share the original data.
If you are interested, please contact: Valérian Rousset

Stainless

Stainless is a tool for verifying Scala programs developed by the LARA. It can verify that your program is correct for all inputs, it can report inputs for which your program fails when they exist, and it can prove that functions do not loop. As an example application, Stainless can perform verification of Smart Contracts.
If you are interested, please contact: Christian Grigis