EDAMS: Efficient Data Anonymization Model Selector for Privacy-Preserving Data Publishing

Abstract

The evolution of internet to the Internet of Things (IoT) gives an exponential rise to the data collection process. This drastic increase in the collection of a person’s private information represents a serious threat to his/her privacy. Privacy-Preserving Data Publishing (PPDP) is an area that provides a way of sharing data in their anonymized version, i.e. keeping the identity of a person undisclosed. Various anonymization models are available in the area of PPDP that guard privacy against numerous attacks. However, selecting the optimum model which balances utility and privacy is a challenging process. This study proposes the Efficient Data Anonymization Model Selector (EDAMS) for PPDP which generates an optimized anonymized dataset in terms of privacy and utility. EDAMS inputs the dataset with required parameters and produces its anonymized version by incorporating PPDP techniques while balancing utility and privacy. EDAMS is currently incorporating three PPDP techniques, namely k-anonymity, l-diversity, and t-closeness. It is tested against different variations of three datasets. The results are validated by testing each variation explicitly with the stated techniques. The results show the effectiveness of EDAMS by selecting the optimum model with minimal effort.