  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.
