Patent ID: 11874951
Assignee: TATA CONSULTANCY SERVICES LIMITED
Field: Computer technology (Electrical engineering)
Classification: CPC G  H | IPC G

Claim 5:
6. The processor implemented method of claim 1, wherein systematically applying the PBKA using the greedy model comprises:
initializing a set of cluster centers from the set of frequent records;
until a stopping criteria is met, performing a plurality of iteration comprising:
selecting a record randomly from amongst the set of remaining records and the set of risky records, wherein during the plurality of iterations, the record is selected from the set of remaining records first followed by selection from the set of set of risky records;
computing, for each record of the plurality of records, a distance metric of each record from set of cluster centers, and adding each record to a cluster from amongst the set of clusters that is associated with a minimum value of the distance metric;
computing a sum of mean square loss for each of the clusters formed and comparing the sum of mean square loss of current iteration with a previous iteration, wherein the mean square loss for the each cluster is computed as sum of squared differences of each point in a cluster with the mean of that cluster followed by updating the cluster centers as the mean of that cluster;
on determination of the sum of the mean square loss for a cluster in the current iteration less than or equal to the mean square loss in the previous iteration, saving the cluster associated with the current iteration; and
performing, for each cluster of the set of clusters, pattern mapping to form the m-sets in each cluster based on the generalized information loss, wherein for each record, patterns are identified for generalization,
said patterns are obtained dynamically by combining different levels of taxonomy trees for the selected set of quasi-identifiers and for each identified pattern generalized information loss is obtained, and
wherein a pattern resulting in a minimal loss is mapped to the record, and
wherein the stopping criteria comprises one of an exhaustion of the plurality of iterations and determination that the mean square error loss is constant in a predefined set of iterations from amongst the plurality of iterations.