Differential Privacy and k-Anonymity-Based Privacy Preserving Data Publishing Scheme With Minimal Loss of Statistical Information

Though anonymization mechanisms have made huge progress in fostering the secondary use of data, it is still very challenging to obtain adequate knowledge from anonymized data while preserving privacy. Most existing mechanisms anonymize entire sections of data and fail to maximally preserve the structure/values of real data. Consequently, the performance of those mechanisms and the output (i.e., the anonymized data) remain problematic in real-life scenarios due to the extensive and unneeded anonymization applied. To address these issues, we propose and implement a hybrid (differential privacy (DP) and
$k$
-anonymity) anonymization scheme that produces supreme-quality anonymized data that offers knowledge similar to real data without compromising privacy. Specifically, we implement a pair of algorithms that divide the dataset into privacy-violating and nonprivacy-violating partitions. Afterward, in a nonprivacy-violating partition, a relaxed privacy budget
$\epsilon $
is applied to numerical attributes, but most of the categorical attributes are retained (as is) for informative analysis. In privacy-violating partitions, fewer changes are applied to the data by using a reasonable value for
$\epsilon $
and by exploiting the diversity in sensitive information. Experiments are conducted on three real-life datasets to prove the feasibility of our scheme for futuristic AI applications. Compared with state-of-the-art (SOTA) methods, our scheme preserves 60.81% of the originality in the anonymized data. The privacy risks are reduced by 20.05%, and utility is enhanced by 54.01% and 15.33% based on information loss (IL) and accuracy metrics. Furthermore, the time overhead is
$3.13\times $
lower than the SOTA methods.