Electronic Thesis and Dissertation Repository

Thesis Format

Integrated Article

Degree

Doctor of Philosophy

Program

Electrical and Computer Engineering

Supervisor

Essex, Aleksander

Abstract

The reduced cost of genome sequencing opened a vast potential for genetic research. However, ethical and privacy concerns prohibit the free sharing of genomic data across institutions. Homomorphic encryption(HE) enables us to perform computations on encrypted data. Our research aims to develop better protocols for performing genomic data analysis while keeping sensitive information secure, focusing on improving their security, communication overhead, and computational complexity. We divide our research into three areas. (1) Secure Function Extensions to Additively HE Cryptosystems (SFE) research presented a novel approach to extend the functionality of additively HE schemes. This approach lets us securely compute functions with a finite integer domain mapped to a binary range. Our results indicate that this extension makes linear HE schemes practical for secure database query and PPML applications, offering a less computationally intense alternative to FHE. (2) Secure database querying performs secure searches in encrypted genomic databases and ensures the querier's and database owner's privacy. Our application achieves the required functionality in a single communication round compared to previous work by searching 100,000 records in under 35 seconds. (3) Privacy-Preserving Machine Learning(PPML) under two-party setting: (a) Autoencoders for secure genotype imputation that use FHE for security and quantization-aware training for optimization. Our results achieved better accuracy than related work; (b) TransPHErmer is the first secure transformer inference protocol built entirely on additively HE, ensuring that no intermediate results are exposed to either of the parties. We introduce a novel thresholded softmax attention mechanism, which eliminates the need for approximations when working with encrypted data and achieves ideal accuracy levels with significantly reduced communication overhead.

Summary for Lay Audience

The advent of new genomic technologies, such as cheaper sequencing methods and in- silico alternatives, has made human genomic data more available and accessible. Genomic data is susceptible to attacks, necessitating us to use sophisticated technologies to keep it secure. Using traditional encryption technologies on genomic data deprives the medical field of essential data, contributing to cutting-edge, personalized treatments. Homomorphic encryption enables us to perform computations on encrypted data while keeping it secure. This thesis explores the application of homomorphic encryption for various crucial applications over genomic data. We achieved three objectives through our research:

• We developed a novel approach to perform a secure evaluation of any function that maps an integer to a binary domain within a single round of communication while not revealing any intermediate results. This approach enabled us to extend the functionality of homomorphic cryptosystems at no additional cost.

• We presented a novel approach to perform a secure search across genomic databases where the query and database remain encrypted. Our results demonstrate the scalability of our application in real-world settings.

• We developed two novel privacy-preserving techniques to perform inference on encrypted data using pre-trained deep-learning models: (a) To perform imputation of encrypted genotype data using autoencoders. (b) To perform inference on encrypted inputs using a pre-trained transformer model. Our approaches allow us to perform inference for a significantly low communication cost.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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