Trade-off between Privacy, Quality and Risk: Anonymization Strategy Evaluation for Data Warehouses
Sascha Schiegg, Armin Gerl · 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) · 2022
The transformation of big data to the cloud requires us to reconsider trust. Trust in all parties involved in the data management, the infrastructure as well as all groups with an access interest. A common way to mitigate the risk of the identification of individuals in case of privacy breaches is anonymization, which consequently also leads to information loss. Depending on the assumed level of confidence, a data processor can control the risk for privacy breaches in changing the point where anonymization gets applied. We examined anonymization points in data warehouse scenarios to evaluate their effects on utility and re-identification risk. Our evaluation showed that data quality can differ up to 4,80% while the re-identification risk is reduced by up to 16,82 %. With still improved quality, the re-identification risk differs up to 53,49 % in another configuration.