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Data Mining and Knowledge Discovery Handbook, 2 Edition part 72

Data Mining and Knowledge Discovery Handbook, 2 Edition part 72. Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data. Data Mining and Knowledge Discovery Handbook, 2nd Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery. | 690 Vicenç Torra 35.2.1 Computation-Driven Protection Procedures the Cryptographic Approach As stated above cryptographic protocols are often applied in applications where the analysis or function to be computed from the data is known. In fact it is usually applied to scenarios with multiple data sources. We illustrate below this scenario with an example. Example 1. Parties Pi . Pn own databases DBi . DBn. The parties want to compute a function say f of these databases i.e. f DBi . DBn without revealing unnecessary information. In other words after computing f DBi . DBn and delivering this result to all Pi what Pi knows is nothing more than what can be deduced from his DBi and the function f. So the computation of f has not given Pi any extra knowledge. Distributed privacy preserving data mining is based on the secure multiparty computation which was introduced by A. C. Yao in 1982 Yao 1982 . For example Lindell and Pinkas 2000 and Lindell and Pinkas 2002 defined a method based on cryptographic tools for computing a decision tree from two data sets owned by two different parties. Bunn and Ostrovsky 2007 discusses clustering data from different parties. When data is represented in terms of records and attributes two typical scenarios are considered in the literature vertical partitioning of the data and horizontal partitioning. They are as follows. Vertically partitioned data. All data owners share the same records but different data owners have information about different attributes i.e. different data owners have different views of the same records or individuals . Horizontally partitioned data. All data owners have information about the same attributes nevertheless the records or individuals included in their data bases are different. As stated above for both centralized and distributed PPDM the only information that should be learnt by the data owners is the one that can be inferred from his original data and the final computed analysis. In this setting the .