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. This paves the way for significant new approaches in medicine, but also poses major challenges for storing, processing and transmitting such big amounts of data in compliance with recent regulations concerning user privacy. We designed and implemented $ER$-index, a new full-text index in minute space which was optimized for pattern-search on compressed and encrypted genomic data using a reference sequence, and that complements a previous index for reference-free genomics. Thanks to a multi-user and multiple-keys encryption model, a single $ER$-index can store the sequences related to a large population of individuals so that users may perform search operations directly on compressed data and only on the sequences to which they were granted access.

Tests performed of three different computing platforms show that the $ER$-index get very good compression ratios and search times, outperforming in many cases a reference tool that was proved nearly-optimal in time and space and does not implement encryption.

The $ER$-index C++ source code plus scripts and data to assess the tool performance are available at: https://github.com/EncryptedIndexes/erindex.

正在创建和设计用于存储基因组信息的巨大DBMS，以对人类及其疾病进行大规模，全面和深入的分析。这为医学上重要的新方法铺平了道路，但同时又要符合有关用户隐私的最新法规，对存储，处理和传输如此大量的数据提出重大挑战。我们设计并实施$Ë\mathrm{[R}$-index，这是一种新的在分钟空间内的全文索引，已针对使用参考序列的压缩和加密基因组数据进行模式搜索进行了优化，并且对先前的无参考基因组索引进行了补充。得益于多用户和多密钥加密模型，单个$Ë\mathrm{[R}$-index可以存储与大量个人有关的序列，以便用户可以直接对压缩数据执行搜索操作，而仅对他们被授予访问权限的序列执行搜索操作。

对三种不同的计算平台进行的测试表明， $Ë\mathrm{[R}$-index获得了很好的压缩率和搜索时间，在许多情况下都优于在时间和空间上几乎最佳且未实现加密的参考工具。

的 $Ë\mathrm{[R}$-index C ++源代码以及用于评估工具性能的脚本和数据可在以下网址获得：https：//github.com/EncryptedIndexes/erindex。