The role of Anonymization Techniques in Differential Privacy

Anonymization Techniques are among the main approaches for keeping people’s information private in the digital world, where the security of confidential data is paramount. While k-anonymity, l-diversity, and t-closeness have traditionally been applied to avoid direct re-identification through anonymization, these approaches cannot help avoid indirect re-identification using auxiliary data. Complementary to these are mathematical frameworks injecting controlled noise, such as differential privacy (DP), to protect data against reasoning attacks. This work has aimed to integrate DP within state-of-the-art anonymization techniques and has given case examples showing improved privacy without loss of data utility. Such a detailed inquiry into the advantages and disadvantages of these hybrid techniques will emphasize the possible utilization in business sectors like healthcare, banking, and location-based services.