Efficient Secure Computation of Edit Distance on Genomic Data

Andrea Migliore, Stelvio Cimato, Gabriella Trucco

2024

Abstract

Genetic data are the most sensitive information for a person, containing many specific features that uniquely determine an individual and also make it possible to trace relationships with other people or evaluate the predisposition to particular diseases. For this reason, any processing of genetic data should be carefully performed and any threat to their privacy properly considered. A very important computation in medical and public health domains involves the evaluation of the edit distance between human genomes, that can eventually lead to a better diagnosis of several diseases. To maintain the privacy of the genetic data, it is possible to apply secure computation protocols and then, in this context, the improvement of the computational performance of such techniques is a key factor for real-world application scenarios. In this paper we focus on the application of the garbling circuit technique for the computation of the edit distance, showing its efficiency. We apply the technique considering four different algorithms and compare their performances to the best previous results found in literature. We show that the Ukkonen algorithm with generalized cut-off is the one that performed better among the considered algorithms, reporting some experimental results obtained considering datasets composed of both randomly generated and real genomic strings.

Paper Citation

in Harvard Style

Migliore A., Cimato S. and Trucco G. (2024). Efficient Secure Computation of Edit Distance on Genomic Data. In Proceedings of the 10th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP; ISBN 978-989-758-683-5, SciTePress, pages 878-883. DOI: 10.5220/0012459400003648

in Bibtex Style

@conference{icissp24,
author={Andrea Migliore and Stelvio Cimato and Gabriella Trucco},
title={Efficient Secure Computation of Edit Distance on Genomic Data},
booktitle={Proceedings of the 10th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP},
year={2024},
pages={878-883},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012459400003648},
isbn={978-989-758-683-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP
TI - Efficient Secure Computation of Edit Distance on Genomic Data
SN - 978-989-758-683-5
AU - Migliore A.
AU - Cimato S.
AU - Trucco G.
PY - 2024
SP - 878
EP - 883
DO - 10.5220/0012459400003648
PB - SciTePress