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|Title:||Privacy Preserving Data Mining, Concepts, Techniques and Evaluation Methodologies|
|Authors:||NAI FOVINO IGOR|
|Publisher:||Information Science Reference|
|Type:||Articles in books|
|Abstract:||Intense work in the area of data mining technology and in its applications to several domains has resulted in the development of a large variety of techniques and tools able to automatically and intelligently transform large amounts of data in knowledge relevant to users. The use of these techniques is today the only effective way to extract useful knowledge from the increasing number of very large databases, that, because of their size, cannot be manually analyzed. However, as with other kinds of useful technologies, the knowledge discovery process can be misused. It can be used, for example, by malicious subjects in order to reconstruct sensitive information for which they do not have an explicit access authorization. Consider a database that has to be shared among several users. Some data contained in the database are protected using access control methods in order to guarantee that only authorized people are allowed to have access to these sensible information. This type of “attack” cannot easily be detected, because, usually, the data used to guess the protected information, is freely accessible. For this reason, many research efforts have been recently devoted to addressing the problem of privacy preserving in data mining. As a result, different sanitization techniques have been proposed for hiding sensitive items or patterns by removing some of the data to be released or inserting noise into data. The mission of this chapter is therefore to introduce the reader in this new research field and to provide the proper instruments (in term of concepts, techniques and example) in order to allow a critical comprehension of the advantages, the limitations and the open issues of the Privacy Preserving Datamining Techniques.|
|JRC Institute:||Institute for the Protection and Security of the Citizen|
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