Data Privacy Protection - Concealing Text and Audio with a DNA-inspired Algorithm



Nowadays, with an increasing amount of personal and confidential
data being transmitted and stored online, entities who store and
manage data need to assure certain guarantees of data privacy protection.
As such, we start by presenting a state of the art review of anonymization
and concealing techniques. Their characteristics and capabilities are
described, as well as metrics and tools to implement and evaluate data
anonymization and concealing. Then, an evaluation of the applicability
of the DNA-inspired information concealing algorithm is made. Usually,
various metrics are used to measure aspects like disclosure risk or utility
of the anonymized data. In this work, we use the Cosine Similarity
metric to measure the similarity between the original data and respective
versions after application of the algorithm. The evaluation is made
by analyzing the output of the algorithm as well as the performance of
the algorithm itself. With the final results and analysis, it is possible
to determine its overall applicability with text and audio files. There is
a discussion with advantages and disadvantages of this and other algorithms,
as well as an identification of problems and respective suggestions
for improvements on data privacy protection methods.


Data Concealing, Data Privacy


Data Privacy

Related Project

EUBra-BIGSEA: Europe – Brazil Collaboration of BIG Data Scientific Research through Cloud-Centric Applications


12th International Conference on Autonomous Infrastructure, Management and Security 2018, June 2018

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