Dynamic distributed KCi‐slice data publishing model with multiple sensitive attributes

SUMMARYPrivacy‐preserving data publishing (PPDP) provides scope for performing various types of analytics by the researcher on the published data without revealing the individuals' privacy. However, it is necessary to consider the data from all the available locations to obtain accurate results from the published data. Then only the derived associations from the published data are helpful to make new decisions by an organization. Existing PPDP models on multiple sensitive attributes like KC‐slice (K stands for bucket size, C stands for constant threshold), KCi‐slice (K–bucket size, Ci–variable threshold), Novel KCi‐slice, and Optimal KCi‐slice models are concentrated only when data is located at a single location. This research work introduces a new model called “distributed KCi‐slice,” which considers the data with multiple sensitive attributes from various locations to obtain accurate results. Initially, this model proposes a novel technique called a distributed algorithm to merge the different data sets into a single data set. It combines the data differently, so that it is not possible to identify any record of a specific data set from the merged data. The proposed model also uses a new bucketization algorithm named (l,m,d)*‐anonymity (l stands for distinct semantic categories of the sensitive values of a sensitive attribute in a specific bucket, m stands for the number of sensitive attributes, and d stands for different sensitive categories for the sensitive values of a sensitive attribute in each sensitive bucket) for the bucketization of the tuples into the buckets. It imposes the privacy constraints only on high sensitive values of each sensitive attribute by considering the semantic and sensitive levels of sensitive values. The distributed KCi‐slice model finally publishes the data in the form of multiple sensitive tables and one quasi table. This model is implemented based on attribute sensitiveness to enhance the utility and privacy levels of the published data.