PRIMAL

The primary focus of this research is on improving techniques for secure multi-party computation (MPC). Secure multi-party computation involves computationally distrustful parties wishing to jointly compute a function while protecting the privacy of their individual inputs.  This allows the computation of sensitive data without disclosing it. MPC allows designing cryptographic solutions that, for instance, enable querying a remote database without revealing the query to the server, enhance anonymity over the internet, or allow medical research on genomics across organizations without disclosing the patients’ DNA. The development of secure computation techniques will allow better protection of the privacy of individuals.  

 

 

The primary objective of this research is to develop privacy-preserving techniques with an emphasis on applications in machine-learning algorithms and data mining. Our results, however, evolved to prioritize foundational work on privacy-preserving techniques with broader applicability beyond machine learning. Our research encompasses a diverse spectrum, delving into fundamental aspects of secure computation and drawing insights from disciplines such as algorithms, data structures, and distributed computing.

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This project has been funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 891234. 

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