Date of Degree
fully homomorphic encryption, data privacy, medical information systems
A wealth of medical data is inaccessible to researchers and clinicians due to privacy restrictions such as HIPAA. Clinicians would benefit from access to predictive models for diagnosis, such as classification of tumors as malignant or benign, without compromising patients’ privacy. In addition, the medical institutions and companies who own these medical information systems wish to keep their models private when used by outside parties.
Fully homomorphic encryption (FHE) enables practical polynomial computation over encrypted data. This dissertation begins with coverage of speed and security improvements to existing private-key fully homomorphic encryption methods. Next this dissertation presents a protocol for third-party private search using private-key FHE. Finally, fully homomorphic protocols for polynomial machine learning algorithms are presented using privacy-preserving Naive Bayes and Decision Tree classifiers. These protocols allow clients to privately classify their data points without direct access to the learned model. Experiments using these classifiers are run using publicly available medical data sets.
These protocols are applied to the task of privacy-preserving classification of real-world medical data. Results show that private-key fully homomorphic encryption is able to provide fast and accurate results for privacy-preserving medical classification.
Wood, Alexander N., "Private-Key Fully Homomorphic Encryption for Private Classification of Medical Data" (2018). CUNY Academic Works.