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Top-down specialization for information and privacy preservation Fung, B.C.M.; Wang, K.; Yu, P.S. Data Engineering, 2005. ICDE 2005. Proceedings. 21st International Conference on Volume , Issue , 5-8 April 2005 Page(s): 205 - 216 Digital Object Identifier 10.1109/ICDE.2005.143 Summary: Releasing person-specific data in its most specific state poses a threat to individual privacy. This paper presents a practical and efficient algorithm for determining a generalized version of data that masks sensitive information and remains useful for modelling classification. The generalization of data is implemented by specializing or detailing the level of information in a top-down manner until a minimum privacy requirement is violated. This top-down specialization is natural and efficient for handling both categorical and continuous attributes. Our approach exploits the fact that data usually contains redundant structures for classification. While generalization may eliminate some structures, other structures emerge to help. Our results show that quality of classification can be preserved even for highly restrictive privacy requirements. This work has great applicability to both public and private sectors that share information for mutual benefits and productivity. » View citation and abstract



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