ER-index: a referential index for encrypted genomic databases
Abstract
Huge DBMSs storing genomic information are being created and engineerized for doing large-scale, comprehensive and in-depth analysis of human beings and their diseases. However, recent regulations like the GDPR require that sensitive data are stored and elaborated thanks to privacy-by-design methods and software. We designed and implemented ER-index, a new full-text index in minute space which was optimized for compressing and encrypting collections of genomic sequences, and for performing on them fast pattern-search queries. Our new index complements the E2FM-index, which was introduced to compress and encrypt collections of nucleotide sequences without relying on a reference sequence. When used on collections of highly similar sequences, the ER-index allows to obtain compression ratios which are an order of magnitude smaller than those achieved with the E2FM-index, but maintaining its very good search performance. Moreover, thanks to the ER-index multi-user and multiple-keys encryption model, a single index can store the sequences related to a population of individuals so that users may perform search operations only on the sequences to which they were granted access. The ER-index C++ source code plus scripts and data to assess the tool performance are available at: https://github.com/EncryptedIndexes/erindex.
Cite
@article{arxiv.1910.02851,
title = {ER-index: a referential index for encrypted genomic databases},
author = {Ferdinando Montecuollo and Giovannni Schmid},
journal= {arXiv preprint arXiv:1910.02851},
year = {2020}
}
Comments
27 pages with detailed pseudocodes