Data Analysis Systems in IoE Environments for Managing Privacy and Data Protection: Pseudonymity, De-Anonymization and the Right to Be Forgotten

One of the most pressing concerns surrounding Big Data is protecting individuals' privacy, asprocessing 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 toprotect large data analytics systems from re-identification risks, this article lays forth amethodology for anonymization. You may employ anonymization methods and models at twophases of this framework, which is based on anonymization policies: during the ETL process andbefore exporting the statistical findings of data analytics. The second step is to assess thelikelihood of data re-identification and, if needed, raise the anonymity level. Although this paperpresents a general framework, Ophidia was used as a case study to demonstrate how it wasimplemented.