Secure Data Protection Using Slicing as a Confusion Technique

Authors(2) :-V Veda Sahithi, V Swarna Kamalam

Data Mining deals with automatic extraction of previously unknown patterns from large amounts of data sets. These data sets typically contain sensitive individual information or critical business information, which consequently get exposed to the other parties during Data Mining activities. Secure data protection has been one of the greater concerns in data mining. Several anonymization techniques, such as generalization and bucketization, have been designed for privacy protective microdata publishing. The generalization loses considerable amount of information, especially for high dimensional data. Bucketization, on the other hand, does not prevent membership disclosure and does not apply for data that do not have a clear separation between quasi-identifying attributes and sensitive attributes. Solution to this problem is provided by we introduce a novel data anonymization technique called slicing to improve the current state of the art.

Authors and Affiliations

V Veda Sahithi
Information Technology Department, JNTUH University/Bhoj Reddy Engineering College for Women, Hyderabad, Telangana, India
V Swarna Kamalam
Information Technology Department, JNTUH University/Bhoj Reddy Engineering College for Women, Hyderabad, Telangana, India

Data Mining, Privacy Protection, Data anonymization, Security, L diversity.

  1. C. Aggarwal, “On k-Anonymity and the Curse of Dimensionality,” Proc. Int’l Conf. Very Large Data Bases (VLDB), pp. 901-909, 2005.
  2. A. Blum, C. Dwork, F. McSherry, and K. Nissim, “Practical Privacy: The SULQ Framework,” Proc. ACM Symp. Principles of Database Systems (PODS), pp. 128-138, 2005.
  3. J. Brickell and V. Shmatikov, “The Cost of Privacy: Destruction of Data-Mining Utility in Anonymized Data Publishing,” Proc. ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (KDD), pp. 70-78, 2008.
  4. B.-C. Chen, K. LeFevre, and R. Ramakrishnan, “Privacy Skyline: Privacy with Multidimensional Adversarial Knowledge,” Proc. Int’l Conf. Very Large Data Bases (VLDB), pp. 770-781, 2007.
  5. H. Cramt’er, Mathematical Methods of Statistics. Princeton Univ. Press, 1948.
  6. I. Dinur and K. Nissim, “Revealing Information while Preserving Privacy,” Proc. ACM Symp.

Publication Details

Published in : Volume 5 | Issue 4 | July-August 2019
Date of Publication : 2019-08-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 297-301
Manuscript Number : CSEIT1953193
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

V Veda Sahithi, V Swarna Kamalam, "Secure Data Protection Using Slicing as a Confusion Technique ", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 4, pp.297-301, July-August-2019. Available at doi : https://doi.org/10.32628/CSEIT1953193
Journal URL : https://res.ijsrcseit.com/CSEIT1953193 Citation Detection and Elimination     |      |          | BibTeX | RIS | CSV

Article Preview