Pal, A K (2014) Achieving k-anonymity using full domain generalization. MTech thesis.
![]() | PDF 1209Kb |
Abstract
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 |
Repository Staff Only: item control page