| dc.description.abstract | The problem of data compression having specific security properties in order to guarantee
user’s privacy is a living matter. On the other hand, high-throughput systems in genomics
(e.g. the so-called Next Generation Sequencers) generate massive amounts of
genetic data at affordable costs.
As a consequence, huge DBMSs integrating many types of genomic information,
clinical data and other (personal, environmental, historical, etc.) information types are on the
way. This will allow for an unprecedented capability of doing large-scale, comprehensive and
in-depth analysis of human beings and diseases; however, it will also constitute a formidable
threat to user’s privacy.
Whilst the confidential storage of clinical data can be done with well-known methods
in the field of relational databases, it is not the same for genomic data; so the main goal of my
research work was the design of new compressed indexing schemas for the management
of genomic data with confidentiality protection.
For the effective processing of a huge amount of such data, a key point will be the
possibility of doing high speed search operations in secondary storage, directly operating
on the data in compressed and encrypted form; therefore, I spent a big effort to obtain
algorithms and data structures enabling pattern search operations on compressed and encrypted
data in secondary storage, so that there is no need to preload data in main memory
before starting that operations. [edited by Author] | it_IT |
