The week's pick
Reseach Article
Statistical Disclosure Control for Data Privacy Preservation
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 80 - Number 10 |
| Year of Publication: 2013 |
| Authors: Sarat Kumar Chettri, Bonani Paul, Ajoy Krishna Dutta |
10.5120/13899-1880
|
Sarat Kumar Chettri, Bonani Paul, Ajoy Krishna Dutta . Statistical Disclosure Control for Data Privacy Preservation. International Journal of Computer Applications. 80, 10 ( October 2013), 38-43. DOI=10.5120/13899-1880
Abstract
With the phenomenal change in a way data are collected, stored and disseminated among various data analyst there is an urgent need of protecting the privacy of data. As when individual data get disseminated among various users, there is a high risk of revelation of sensitive data related to any individual, which may violate various legal and ethical issues. Statistical Disclosure Control (SDC) is often applied to statistical databases for preserving the privacy of individual data. Microaggregation is an efficient Statistical Disclosure Control perturbative technique for microdata protection i. e. protection of individual data. Unlike k-Anonymity, microaggregation method modifies data without suppressing or generalizing it. But to prevent the disclosure of sensitive data it should not be modified to an extent that the data utility is affected. So, the major challenge is how to perturb the data in such a way that a balance is maintained between data utility and risk of data disclosure. Here in this paper, we have proposed a new SDC method based on multivariate data-oriented microaggregation technique for individual data protection with minimal information loss and low data disclosure risk. Experimental results show that our proposed method proves our claim as when compared with other state-of art existing methods of data protection.
References
- L. Willenborg and T. DeWaal, "Elements of Statistical Disclosure Control", Lecture Notes in Statistics, Springer-Verlag, New York, 2001.
- E. Bertino, D. Lin and W. Jiang, "A Survey of Quantification of Privacy Preserving Data Mining Algorithms", in Privacy Preserving Data Mining, Springer, US, 2008.
- D. Defays and P. Nanopoulos, "Panels of enterprises and confidentiality: The small aggregates method", in 92 Symposium on Design and Analysis of Longitudinal Surveys, Canada, Ottawa, 1993, 195–204.
- D. Defays and N. Anwar, "Micro-aggregation: A generic method, in 2nd International Symposium on Statistical Confidentiality", Eurostat, Luxembourg, 1995, 69–78.
- J. Domingo-Ferrer and J. M. Mateo-Sanz, "Practical data-oriented microaggregation for statistical disclosure control", IEEE Transactions on Knowledge and Data Engineering, Vol. 14, No. 1, 2002, 189–201.
- J. Domingo-Ferrer and V. Torra, "Ordinal, continuous and heterogenerous k-anonymity through microaggregation", Data Mining and Knowledge Discovery, Vol. 11, No. 2, 2005, 195–212.
- V. Torra, "Microaggregation for categorical variables: A median based approach", in J. Domingo-Ferrer and V. Torra Eds. Lecture Notes in Computer Science, Vol. 3050, Springer, Berlin, Heidelberg, 162–174.
- J. Domingo-Ferrer and V. Torra, "Ordinal, continuous and heterogenous k-anonymity through microaggregation", Data Mining and Knowledge Discovery, Vol. 11, No. 2, 2005, 95–212.
- P. Samarati, "Protecting respondents' identities in microdata release", IEEE Trans. Knowledge and Data Engineering, Vol. 13, No. 6, 2001, 1010–1027.
- S. L. Hansen and S. Mukherjee, "A polynomial algorithm for optimal univariate microaggregation", IEEE Transactions on Knowledge and Data Engineering, Vol. 15, No. 4, 2003, 1043–1044.
- A. Oganian and J. Domingo-Ferrer, "On the complexity of optimal microaggregation for statistical disclosure control", Statistical Journal of the United Nations Economic Comission for Europe, Vol. 18, No. 4, 2001, 345–354.
- A. Hundepool, A. V. deWetering, R. Ramaswamy, L. Franconi, A. Capobianchi, P. -P. DeWolf, J. Domingo-Ferrer, V. Torra, R. Brand & S. Giessing, (2003) "?-ARGUS version 3. 2 Software and User's Manual", Voorburg NL: Statistics Netherlands, http://neon. vb. cbs. nl/casc.
- A. Solanas & A. Mart ??nez-Ballest ?e, "V-MDAV: A multivariate microaggregation with variable group size", Seventh COMPSTAT Symposium of the IASC, 2006, Rome.
- J. L. Lin, T. H. Wen, J. C. Hsieh, and P. C. Chang, "Density-based microaggregation for statistical disclosure control", Expert Systems with Applications, Vol. 37, No. 4, 2010, 3256–3263.
- R. Brand, J. Domingo-Ferrer & J. M. Mateo-Sanz,: "Reference data sets to test and compare sdc methods for protection of numerical microdata", European Project IST-2000-25069, 2002, CASC, http://neon. vb. cbs. nl/casc.
- D. Pagliuca,: Some results of individual ranking method on the system of enterprise accounts annual survey. Esprit SDC Project, Deliverable MI-3/ D, 1999.
- S. Chettri, B. Paul & A. Dutta, "A Comparative Study on Microaggregation Techniques for Microdata Protection" International Journal of Data Mining & Knowledge Management Process, Vol 2 (6), 2012, 27–40.
Index Terms
Keywords