Abstract
In an effort to combat the ongoing opioid epidemic in the U.S. many states have adopted a Prescription Drug Management Program (PDMP). In most cases, this program has evolved into central state-wide databases for storing sensitive patient prescription records, intended for use by health care professionals to make more accurate prescribing and dispensing decisions per patient. We outline the security and privacy concerns that arise and propose a solution using privacy-preserving machine learning and fully homomorphic encryption.
Original language | English (US) |
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Title of host publication | Protecting Privacy through Homomorphic Encryption |
Publisher | Springer International Publishing |
Pages | 169-176 |
Number of pages | 8 |
ISBN (Electronic) | 9783030772871 |
ISBN (Print) | 9783030772864 |
DOIs | |
State | Published - Jan 4 2022 |
ASJC Scopus subject areas
- General Mathematics
- General Computer Science
- General Engineering