Literature DB >> 29677926

Enabling Analytics on Sensitive Medical Data with Secure Multi-Party Computation.

Meilof Veeningen1, Supriyo Chatterjea1, Anna Zsófia Horváth2, Gerald Spindler2, Eric Boersma3, Peter van der Spek3, Onno van der Galiën4, Job Gutteling5, Wessel Kraaij6, Thijs Veugen7.   

Abstract

While there is a clear need to apply data analytics in the healthcare sector, this is often difficult because it requires combining sensitive data from multiple data sources. In this paper, we show how the cryptographic technique of secure multi-party computation can enable such data analytics by performing analytics without the need to share the underlying data. We discuss the issue of compliance to European privacy legislation; report on three pilots bringing these techniques closer to practice; and discuss the main challenges ahead to make fully privacy-preserving data analytics in the medical sector commonplace.

Keywords:  Big data; data sharing; general data protection regulation; privacy; privacy-preserving data mining; secure multi-party computation

Mesh:

Year:  2018        PMID: 29677926

Source DB:  PubMed          Journal:  Stud Health Technol Inform        ISSN: 0926-9630


  1 in total

1.  Clinical Research Informatics: Contributions from 2018.

Authors:  Christel Daniel; Dipak Kalra
Journal:  Yearb Med Inform       Date:  2019-08-16
  1 in total

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