Evaluation of secure multi-party computation for reuse of distributed electronic health data

Abstract

There has been an increasing need for reuse of health data (i.e. research, quality assurance, public health, and commercial applications). However, privacy and legal issues have limited the reuse. Several privacy-preserving techniques (both centralized and distributed) have been developed to allow reuse of health data while preserving privacy. The distributed techniques enable institutions to jointly compute on their private data while preserving the privacy of their data. However, the centralize approach applies perturbation or anonymization technique on the private data before giving out the data for computation. This paper presents criteria, such as privacy level, linkability support, efficiency and scalability, to evaluate distributed privacy preserving techniques.