Events

Machine Learning and Pattern Recognition on Encrypted Medical and Bioinformatics Data

Speaker: Delaram Kahrobaei

Location: Rogers Hall, Tandon School of Engineering, Room 474

Date: Tuesday, February 14, 2023

Machine learning and statistical techniques are powerful tools for analyzing large amounts of medical and genomic data. On the other hand, ethical concerns and privacy regulations prevent free sharing of this data. Encryption techniques such as fully homomorphic encryption (FHE) enable evaluation over encrypted data. Using FHE, machine learning models such as deep learning, decision trees, and Naive Bayes have been implemented for privacy-preserving applications using medical data. These applications include classifying encrypted data and training models on encrypted data. FHE has also been shown to enable secure genomic algorithms, such as paternity and ancestry testing and privacy-preserving applications of genome-wide association studies. I will give a survey an overview of fully homomorphic encryption and its applications in medicine and bioinformatics. The high-level concepts behind FHE and its history will be introduced, and details on current open-source implementations will be provided. The state of fully homomorphic encryption for privacy-preserving techniques in machine learning and bioinformatics will be reviewed, along with descriptions of how these methods can be implemented in the encrypted domain.