f-Slip: An Efficient Privacy-Preserving Data Publishing Framework for 1: M Microdata with Multiple Sensitive Attributes.

Abstract Privacy of the individuals plays a vital role when a dataset is disclosed in public. Privacy-preserving data publishing is a process of releasing the anonymized dataset for various purposes of analysis and research. The data to be published contain several sensitive attributes such as diseases, salary, symptoms, etc. Earlier, researchers have dealt with datasets considering it would contain only one record for an individual [1:1 dataset], which is uncompromising in various applications. Later, many researchers concentrate on the dataset, where an individual has multiple records [1:M dataset]. In the paper, a model f-slip was proposed that can address the various attacks such as Background Knowledge (bk) attack, Multiple Sensitive attribute correlation attack (MSAcorr), Quasi-identifier correlation attack(QIcorr), Non-membership correlation attack(NMcorr) and Membership correlation attack(Mcorr) in 1:M dataset and the solutions for the attacks. In f -slip, the anatomization was performed to divide the table into two subtables consisting of i) quasi-identifier and ii) sensitive attributes. The correlation of sensitive attributes is computed to anonymize the sensitive attributes without breaking the linking relationship. Further, the quasi-identifier table was divided and k-anonymity was implemented on it. An efficient anonymization technique, frequency-slicing (f-slicing), was also developed to anonymize the sensitive attributes. The f -slip model is consistent as the number of records increases. Extensive experiments were performed on a real-world dataset Informs and proved that the f -slip model outstrips the state-of-the-art techniques in terms of utility loss, efficiency and also acquires an optimal balance between privacy and utility.