Multiple Sensitive Attributes Based Privacy Preserving Data Publishing

Vanasiwala, Jasmina N; Nanavati, Nirali

doi:10.1109/iccmc.2018.8487483

Cited by 2 publications

(1 citation statement)

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“…In literature, various privacy preservation approaches have been proposed for the trajectory data with single sensitive attributes, such as Generalization, Perturbation, Clustering, Differential Privacy, and Suppression, to defend against the various linkage attacks [12] [13]. Similarly, Multi-sensitive Bucketization, (p,k) -Angelization, and Generalization are the approaches to preserve the privacy of the multiple sensitive attributes along with the Quasi Identifiers but not with trajectory data.…”

Section: Introductionmentioning

confidence: 99%

Reciprocal Bucketization (RB) - An Efficient Data Anonymization Model for Smart Hospital Data Publishing

2024

IJACSA

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With the lightning growth of the Internet of Things (IoT), enormous applications have been developed to serve industries, the environment, society, etc. Smart Health care is one of the significant applications of the IoT, where intelligent environments enrich safety and ease of surveillance. The database of the Smart Hospital records the patient's sensitive information, which could face various potential privacy breaches through linkage attacks. Publishing such sensitive data to society is challenging in adopting the best privacy preservation model to defend against linkage attacks. In his paper, we propose a novel Reciprocal Bucketization Anonymization model as the privacy preservation method to defend against Identity, Attribute, and Correlated Linkage attacks. The proposed anonymization method creates the Buckets of patient records and then partitions the data into sensor trajectory and Multiple Sensitive attributes (MSA). A local suppression is employed on Sensor Trajectory Data and Slicing on MSA to get the anonymized data to be published gathered by combining anonymized sensor trajectory and MSA. The proposed method is validated on the synthetic and real-time dataset by comparing its data utility loss in both sensor trajectory and the MSA. The experimental results eradicate that the RB -Anonymization exhibits the nature of best privacy preservation against Identity, Attribute, and Correlated linkages attacks with negligible utility loss compared with the existing methods.

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Section: Introductionmentioning

confidence: 99%