Abstract. The NIH Homomorphic Encryption and Privacy Enhancing Technologies (PETs) webinar series (https://datascience.nih.gov/homomorphic-encryption-and-privacy-enhancing-technologies-webinar-series) cumulated in an in-person workshop for the webinar presenters to discuss key issues in linking disparate data from disparate data resources (e.g., different data repositories) to conduct large-scale analysis, to train and develop tools for analyzing data, etc. This workshop discussion and future work product will provide insights on how to promote sharing of and integration of data while maintaining confidentiality via homomorphic encryption, other PETs, and challenges in application of techniques including the ethical, legal, and social implication of increasing data sharing and access to data and data containing sensitive information. To date, fully homomorphic encryption schemes are limited by the amount of data that can be encrypted, the accumulation of noise, and the power and speed required to perform encryption and conduct analyses on encrypted data. Workshop participants discussed the limitations of current fully homomorphic encryptions schemes, the challenges needed to overcome these limitations, and applications of other alternatives that would maintain privacy while allowing integration of disparate data across data bases and combining homomorphic encryption with other PETs such as differential privacy and / or block chain technologies.
Joint work with: Jonathan Pollock, Heidi Sophia, Rebecca Rodriguez, Rui Pereira De Sa, Freddie Pruitt, Craig Hayn, Rachel Leffler, Susan Wright, Roger Little, Ishwar Chandramouliswaran, Elaine Collier (NIH)
[Slides] [Video]
