F-Slip: an efficient privacy-preserving data publishing framework for 1:M microdata with multiple sensitive attributes

Abstract

Privacy-preserving data publishing is a process of releasing the anonymized dataset for various purposes of analysis and research. 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 raw table into two sub-tables (1) quasi-identifier and (2) 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, was also developed to anonymize the sensitive attributes. The novel approach in the f-slip model is the slicing of records according to the frequency of occurrences of sensitive attribute values in each sub-table. The workload experiment proves that 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.