Data Analysis Systems in IoE Environments for Managing Privacy and Data Protection: Pseudonymity, De-Anonymization and the Right to Be Forgotten
DOI:
https://doi.org/10.48047/b23v4479Keywords:
Privacy, Anonymization, Data Analytics, Big Data.Abstract
One of the most pressing concerns surrounding Big Data is protecting individuals' privacy, as
processing massive amounts of data might lead to the exposure of private information. Actually,
re-identification via privacy attacks is still possible, even with anonymised data. In order to
protect large data analytics systems from re-identification risks, this article lays forth a
methodology for anonymization. You may employ anonymization methods and models at two
phases of this framework, which is based on anonymization policies: during the ETL process and
before exporting the statistical findings of data analytics. The second step is to assess the
likelihood of data re-identification and, if needed, raise the anonymity level. Although this paper
presents a general framework, Ophidia was used as a case study to demonstrate how it was
implemented.
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