Abstract
Rapid development of data mining technologies has brought a serious threat to the security of private sensitive information. There is unwanted disclosure of private sensitive information during the process of data collection, data publishing and data mining operations. Developing data mining models without accessing private information as well as without compromising the utility of mining result has become a major concern. Various approaches towards Data Perturbation techniques are limited with manipulation of Gaussian noise to the original data. But Gaussian noise is likely prone to linear attacks. In this paper, we study the effect of adding Gaussian and Laplacian noise as a two step process for random data perturbation. We also consider this effect in multiple trust levels, where the data miners receive their perturbed copies only according to their trust levels. We prove that our solution is vigorous against both linear and non linear attacks. Malicious data miner cannot reconstruct the original data by having access to differently perturbed copies of same data. Our solution thus proves that the combination of Gaussian and Laplacian noise can withstand with both linear and non linear attacks.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Asian Journal of Research in Social Sciences and Humanities
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.