Revolutionizing Medical Data Sharing Using Advanced Privacy Enhancing Technologies: Technical, Legal and Ethical Synthesis
James Scheibner, Jean Louis Raisaro, Juan Ram\'on Troncoso-Pastoriza,, Marcello Ienca, Jacques Fellay, Effy Vayena, Jean-Pierre Hubaux

TL;DR
This paper introduces Multiparty Homomorphic Encryption (MHE), a novel privacy technology that enhances secure multisite medical data sharing, complies with GDPR, and improves efficiency over traditional methods, thereby accelerating medical research.
Contribution
It synthesizes advanced PETs, specifically MHE, demonstrating its legal compliance, performance benefits, and potential to replace bespoke contracts in multisite medical data sharing.
Findings
MHE offers a performance advantage over separate HE or SMC.
MHE ensures data processed is considered anonymized under GDPR.
Using MHE reduces reliance on customized data sharing contracts.
Abstract
Multisite medical data sharing is critical in modern clinical practice and medical research. The challenge is to conduct data sharing that preserves individual privacy and data usability. The shortcomings of traditional privacy-enhancing technologies mean that institutions rely on bespoke data sharing contracts. These contracts increase the inefficiency of data sharing and may disincentivize important clinical treatment and medical research. This paper provides a synthesis between two novel advanced privacy enhancing technologies (PETs): Homomorphic Encryption and Secure Multiparty Computation (defined together as Multiparty Homomorphic Encryption or MHE). These PETs provide a mathematical guarantee of privacy, with MHE providing a performance advantage over separately using HE or SMC. We argue MHE fulfills legal requirements for medical data sharing under the General Data Protection…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
