Secure Similarity Queries: Enabling Precision Medicine with Privacy

摘要

Up till now, most medical treatments are designed for average patients. However, one size doesn't fit all, treatments that work well for some patients may not work for others. Precision medicine is an emerging approach for disease treatment and prevention that takes into account individual variability in people's genes, environments, lifestyles, etc. A critical component for precision medicine is to search existing treatments for a new patient by similarity queries. However, this also raises significant concerns about patient privacy, i.e., how such sensitive medical data would be managed and queried while ensuring patient privacy? In this paper, we (1) briefly introduce the background of the precision medicine initiative, (2) review existing secure kNN queries and introduce a new class of secure skyline queries, (3) summarize the challenges and investigate potential techniques for secure skyline queries.