Achieving k-anonymity using full domain generalization

Pal, A K (2014) Achieving k-anonymity using full domain generalization. MTech thesis.



Preserving privacy while publishing data has emerged as key research area in data security and has become a primary issue in publishing person specific sensitive information. How to preserve one's privacy efficiently is a critical issue while publishing data. K-anonymity is a key technique for de-identifying the sensitive datasets. In our work, we have described a framework to implement most of the k-anonymity algorithms and also proposed a novel scheme that produces better results with real-world datasets. Additionally, we suggest a new approach that attains better results by applying a novel approach and exploiting various characteristic of our suggested framework. The proposed approach uses the concept of breadth- search algorithm to generalize the lattice in bottom-up manner. the proposed algorithm generates the paths using predictive tagging of the nodes in the lattice in vertically.the proposed algorithm has less execution time than other full domain generalization algorithms for k-anonymization.

Item Type:Thesis (MTech)
Uncontrolled Keywords:k-anonymity, Data Privacy, domain generalization,Quasi Identifier,data utility etc.
Subjects:Engineering and Technology > Computer and Information Science > Information Security
Divisions: Engineering and Technology > Department of Computer Science
ID Code:5537
Deposited By:Hemanta Biswal
Deposited On:18 Jul 2014 15:04
Last Modified:18 Jul 2014 15:04
Supervisor(s):Babu, K S

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