Abstract
It is obvious that if a data mining algorithm is run against the union of the databases, and its output becomes known to one or more of the parties, it reveals something about the contents of the other databases. Research in secure distributed computation, which was done as part of a larger body of research in the theory of cryptography, has achieved remarkable results. These results were shown using generic constructions that can be applied to any function that has an efficient representation as a circuit. We describe these results, discuss their efficiency, and demonstrate their relevance to privacy preserving computation of data mining algorithms. Note that we consider here a distributed computing scenario, rather than a scenario where all data is gathered in a central server, which then runs the algorithm against all data. (The central server scenario introduces interesting privacy issues, too, but they are outside the scope of this paper.)
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Sharma, A., Ojha, V. (2010). Privacy Preserving Data Mining by Cyptography. In: Meghanathan, N., Boumerdassi, S., Chaki, N., Nagamalai, D. (eds) Recent Trends in Network Security and Applications. CNSA 2010. Communications in Computer and Information Science, vol 89. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14478-3_58
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DOI: https://doi.org/10.1007/978-3-642-14478-3_58
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