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

Privacy-preserving data publishing is a process of releasing the anonymized dataset for various purposes analysis and research. Earlier, researchers have dealt with datasets considering it would contain only one record an individual [1:1 dataset], which uncompromising in applications. Later, many concentrate on dataset, where has multiple records [1:M dataset]. In paper, model f-slip was proposed that can address attacks such as Background Knowledge (bk) attack, Multiple Sensitive attribute correlation attack (MSAcorr), Quasi-identifier attack(QIcorr), Non-membership attack(NMcorr) Membership attack(Mcorr) 1:M solutions attacks. f-slip, anatomization performed to divide raw table into two sub-tables (1) quasi-identifier (2) sensitive attributes. The attributes computed anonymize without breaking linking relationship. Further, divided k-anonymity implemented it. An efficient anonymization technique, frequency-slicing, also developed novel approach slicing according frequency occurrences values each sub-table. workload experiment proves consistent number increases. Extensive experiments were real-world Informs proved outstrips state-of-the-art techniques terms utility loss, efficiency acquires optimal balance between privacy utility.