In today's electronic world, everyday data is being generated every minute, for every online transaction, in all domains. Since data being very important for any business domains, masking data is a very important entity. Now in most of the enterprises all of these data are stored in a Data warehouse. Since Data warehouse stores the sensitive data to any business enterprises, this is a common target for the hackers to leak the data. Therefore providing security to these Data warehouses is a challenging task. Now we propose a Data masking technique that will protect these Data warehouses. Data masking is a technique in which we replace the original set of data with another set of data that is not real but realistic. The numeric data is masked using a mathematical formula that makes use of modulus operator. Here we also make use of injecting false rows that increases the overall data security strength by creating randomness in the data. Implementation of this method is done on a real-world data warehouse and is implemented on oracle 10g and the results show that the method is a better onethan the existing solution.

1. Ricardo Jorge Santos, Jorge Bernardino, Marco Vieira "Balancing Security and Performance for Enhancing Data Privacy in Data warehouses", CISUC – DEI – FCTUC, 2012.
2. A Net 2000 Ltd. White Paper "Data masking: What You Need to Know Before You Begin".
3. Tanya Baccam "SANS Institute Product Review: Oracle Data masking", a SANS Whitepaper, January 2012.
4. Ravi Kumar G K, Dr. B. Justus Rabi, Dr. Ravindra S. Hegadi, Archana R A "Experimental Study of Various Data masking Techniques with Random Replacement using data volume", International Journal of Computer Science and Information Security, Vol. 9, No. 8, August 2011.
5. Ravi Kumar G K, Manjunath T N, Ravindra S Hegadi, Umesh I M "A Survey on Recent Trends, Process and Development in Data masking for Testing", IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 2, March 2011.
6. Adam N. R. , and Wortmann, J. C. 1989. "Security-Control Methods for Statistical Databases:A Comparative study," ACM Computing Surveys (21:4), pp. 515 –556.
7. Manjunath T. N, Ravindra S Hegadi, Ravikumar G K. "A Survey on Multimedia Data Mining and Its Relevance Today" IJCSNS. Vol. 10 No. 11 pp. 165-170.
8. Manjunath T. N, Ravindra S Hegadi, Ravikumar G K. "Analysis of Data Quality Aspects in Datawarehouse Systems", (IJCSIT)
9. International Journal of Computer Science and Information Technologies, Vol. 2 (1), 2010, 477-485
10. Dalenius, T. , and Reiss, S. P. 1982. "Data Swa`pping: A Technique for Disclosure Control," Journal of Statistical Planning and Inference (6:1),pp. 73-85.
11. Domingo-Ferrer J. , and Mateo-Sanz, J. M. 2002. "Practical Data- Oriented Microaggregation for Statistical Disclosure Control," IEEE Transactions on Knowledge and Data Engineering (14:1), pp. 189- 201.
12. Epstein, R. A. 2002. "HIPAA on Privacy: Its Unintended and Intended Consequences," Cato Journal (22:1), Spring/Summer, pp. 13-19.
13. Greengard, S. 1996. "Privacy: Entitlement or Illusion?" Personnel Journal (75:5), pp. 74-88.
14. Kaelber, D. , and Jha, A. 2008. "A Research Agenda for Personal Health Records (PHRs),"
15. Journal of the American Medical Informatics Association (15:6), November / December.
16. KDnuggets, "Google Subpoena: Child Protection vs. Privacy," Accessed July 2006, from Http://www. kdnuggets. com/polls/2006/google_subpoena. htm.
17. Liew, C. K. , Choi, U. J. , and Liew, C. J. 1985. "A Data Distortion by Probability Distribution,"ACM Transactions on Database Systems (10:3), pp. 395-411.
18. Xiao-Bai Li, Luvai Motiwalla BY "Protecting Patient Privacy with Data Masking" WISP 2009
19. Oracle White Paper—Data Masking Best Practices JULY 2010
20. Sachin Lodha BY Data Privacy – TRDDC Silver Jubilee Commemoration Publication – SL Comments. doc

Data Warehouse Data Masking Encryption Data Security.