Secure Genomic String Search with Parallel Homomorphic Encryption
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Published:2024-01-11
Issue:1
Volume:15
Page:40
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ISSN:2078-2489
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Container-title:Information
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language:en
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Short-container-title:Information
Author:
Aziz Md Momin Al1ORCID, Tamal Md Toufique Morshed1, Mohammed Noman1ORCID
Affiliation:
1. Department of Computer Science, University of Manitoba, Winnipeg , MB R3T 5V6, Canada
Abstract
Fully homomorphic encryption (FHE) cryptographic systems enable limitless computations over encrypted data, providing solutions to many of today’s data security problems. While effective FHE platforms can address modern data security concerns in unsecure environments, the extended execution time for these platforms hinders their broader application. This project aims to enhance FHE systems through an efficient parallel framework, specifically building upon the existing torus FHE (TFHE) system chillotti2016faster. The TFHE system was chosen for its superior bootstrapping computations and precise results for countless Boolean gate evaluations, such as AND and XOR. Our first approach was to expand upon the gate operations within the current system, shifting towards algebraic circuits, and using graphics processing units (GPUs) to manage cryptographic operations in parallel. Then, we implemented this GPU-parallel FHE framework into a needed genomic data operation, specifically string search. We utilized popular string distance metrics (hamming distance, edit distance, set maximal matches) to ascertain the disparities between multiple genomic sequences in a secure context with all data and operations occurring under encryption. Our experimental data revealed that our GPU implementation vastly outperforms the former method, providing a 20-fold speedup for any 32-bit Boolean operation and a 14.5-fold increase for multiplications.This paper introduces unique enhancements to existing FHE cryptographic systems using GPUs and additional algorithms to quicken fundamental computations. Looking ahead, the presented framework can be further developed to accommodate more complex, real-world applications.
Funder
Natural Sciences and Engineering Research Council
Subject
Information Systems
Reference56 articles.
1. Gentry, C. (31–2, January 31). Fully homomorphic encryption using ideal lattices. Proceedings of the STOC, Bethesda, MD, USA. 2. Pham, A., Dacosta, I., Endignoux, G., Pastoriza, J.R.T., Huguenin, K., and Hubaux, J.P. (2017, January 16–18). ORide: A Privacy-Preserving yet Accountable Ride-Hailing Service. Proceedings of the 26th USENIX Security Symposium (USENIX Security 17), Vancouver, BC, Canada. 3. Kim, M., Song, Y., and Cheon, J.H. (2017). Secure searching of biomarkers through hybrid homomorphic encryption scheme. BMC Med. Genom., 10. 4. Chen, H., Gilad-Bachrach, R., Han, K., Huang, Z., Jalali, A., Laine, K., and Lauter, K. (2018). Logistic regression over encrypted data from fully homomorphic encryption. BMC Med. Genom., 11. 5. Morshed, T., Alhadidi, D., and Mohammed, N. (2018, January 28–30). Parallel Linear Regression on Encrypted Data. Proceedings of the 16th Annual Conference on Privacy, Security and Trust (PST), Belfast, Ireland.
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