Title: An optimal dynamic KC_i-slice model for privacy preserving data publishing of multiple sensitive attributes adopting various sensitivity thresholds

Authors: N.V.S. Lakshmipathi Raju; M.N. Seetaramanath; P. Srinivasa Rao

Addresses: Department of Computer Science and Engineering, GVP College of Engineering(A), Visakhapatnam, 530048, India ' Department of Information Technology, GVP College of Engineering(A), Visakhapatnam, 530048, India ' Department of Computer Science and Systems Engineering, A.U. College of Engineering, Visakhapatnam, 530003, India

Abstract: Optimal KC_i-slice model is an extension of KC-slice, KC_i-slice and Novel KC_i-slice models for publishing the data with multiple sensitive attributes. The proposed Optimal KC_i-slice model imposes the privacy threshold only on high sensitive values instead of applying on all the sensitive values of each sensitive attribute. It imposes the necessary privacy threshold to each sensitive attribute based on their sensitiveness. It automatically leads to a good utility rate and required privacy levels on all high sensitive values of each sensitive attribute. Optimal KC_i-slice model finishes the data publishing process in two steps. Firstly, it uses the Enhanced semantic l-diversity algorithm to attach the tuples into the buckets and splits the sensitive attributes into several sensitive tables. The second step decides the correlated quasi attributes and also concatenates the correlated quasi attributes with SIDs (Sensitive bucket identifiers) of sensitive buckets. Proposed Optimal KC_i-slice model achieves a high utility rate and required privacy levels compared to all the existing models.

Keywords: KC-slice; high sensitive attributes; low sensitive attributes; suppression; confidentiality; data utility; slicing.

DOI: 10.1504/IJDS.2019.105264

International Journal of Data Science, 2019 Vol.4 No.4, pp.320 - 350

Received: 11 Jan 2019
Accepted: 10 Jan 2020
Published online: 22 Feb 2020 *

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