STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Incognito: efficient full-domain K-anonymity
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Achieving anonymity via clustering
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
How to generate and exchange secrets
SFCS '86 Proceedings of the 27th Annual Symposium on Foundations of Computer Science
PinKDD'07 Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD
ICDT'05 Proceedings of the 10th international conference on Database Theory
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Advances in storage, networks, and hardware technology have resulted in an explosion of data and given rise to multiple sources of overlapping data. This, combined with general apathy towards privacy issues while designing systems and processes, leads to frequent breaches in personal identity and data security. What makes this worse is that many of these breaches are committed by the legitimate users of the data. Major countries like the U.S., Japan, Canada, Australia and EU have come up with strict data distribution laws which demand their organizations to implement proper data security measures that respect personal privacy and prohibit dissemination of raw data outside the country. Since companies are not able to provide real data, they often resort to completely random data. It is obvious that such a data would offer complete privacy, but would have very low utility. This has serious implications for an IT services company like Tata Consultancy Services Ltd. (TCS), since application development and testing environments rely on realistic test data to verify that the applications provide the functionality and reliability they were designed to deliver. It is always desirable that the test data is similar to, if not the same as, the production data. Hence, deploying proven tools that make de-identifying production data easy, meaningful and cost-effective is essential. Data masking methods came into existence to permit the legitimate use of data and avoid misuse. In this paper, we consider various such techniques to come up with a comprehensive solution for data privacy requirements. We present a detailed methodology and solutions for enterprise-wide masking. We also present the data masking product MASKETEERTM, developed at TCS, which implements these techniques for providing maximum privacy for data while maintaining good utility.