Data Anonymization: Techniques and Models

Data growth is exponential and nearly immeasurable. We used to talk about megabytes when we spoke about data, but now we talk about petabytes with BigData. This data growth makes sensitive data and identifiers increasingly exposed. To address this issue, there is anonymization data, which attempts to “mask” the data so that it is nearly difficult to identify and correlate persons with them; yet, the data remains usable for statistical reasons, among other things. To avoid falling behind in these technical difficulties, many businesses employ free, open-source software. However, this software is not always secure or meets the user’s expectations. The goal of OSSpal is to normalize these concerns.