Privacy Preserving for Multiple Sensitive Attributes against Fingerprint Correlation Attack Satisfying c-Diversity

Abstract

Privacy preserving data publishing (PPDP) refers to the releasing of anonymized data for the purpose of research and analysis. A considerable amount of research work exists for the publication of data, having a single sensitive attribute. The practical scenarios in PPDP with multiple sensitive attributes (MSAs) have not yet attracted much attention of researchers. Although a recently proposed technique (p, k)-Angelization provided a novel solution, in this regard, where one-to-one correspondence between the buckets in the generalized table (GT) and the sensitive table (ST) has been used. However, we have investigated a possibility of privacy leakage through MSA correlation among linkable sensitive buckets and named it as “fingerprint correlation fcorr attack.” Mitigating that in this paper, we propose an improved solution “c,k-anonymization” algorithm. The proposed solution thwarts the fcorr attack using some privacy measures and improves the one-to-one correspondence to one-to-many correspondence between the buckets in GT and ST which further reduces the privacy risk with increased utility in GT. We have formally modelled and analysed the attack and the proposed solution. Experiments on the real-world datasets prove the outperformance of the proposed solution as compared to its counterpart.

F-Classify: Fuzzy Rule Based Classification Method for Privacy Preservation of Multiple Sensitive Attributes

“…Weights for SAs cannot be calculated in every case, and weight calculation increases execution time. Khan et al [ 33 ] in (p, k) angelization identify the fingerprint correlation attack and suggest an improved (c, k)-anonymization technique. The innovative KCi-slice [ 34 ] is a KC-slice model enhancement with better privacy and utility requirements.…”

Deep restricted and additive homomorphic ElGamal privacy preservations over big healthcare data

“…To address this issue, dual privacy preservation models were presented (Wang et al , 2018) that not only minimized the error ratio but also reduced information loss in a significant manner. Also, multiple sensitive attributes for which privacy preservation was constructed utilized fingerprint correlation to thwart the attacks were involved (Khan et al , 2020).…”

Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey

“…Set S represents the SA which can be of single type (i.e., disease) or multiple types (i.e., disease and salary) depending upon the scenario. The multiple SA scenario is getting significant attention from the research community in recent years [51]. Plenty of solutions have been proposed for the relational data anonymization considering the available data, SA scenarios (single, multiple), and the PPDP settings.…”