In a wide range of applications, multiple data streams need to be examined together in order to discover trends or patterns existing across several data streams. One common practice is to redirect all data streams into a central place for joint analysis. This “centralized” practice is challenged by the fact that data streams often are private in that they come from different owners. In this paper, we focus on the problem of building a classifier in this context and assume that classification evolves as the current window of streams slides forward. This problem faces two major challenges. First, the many-to-many join relationship of streams will blow up the already fast arrival rate of data streams. Second, the privacy requirement implies that data exchange among owners should be minimal. These considerations rule out all classification methods that require producing the join in the current window.We show that Naive Bayesian Classification (NBC) presents a unique opportunity to address this problem. Our main contribution is to adopt NBC to solve the classification problem for private data streams.
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Xu, Y., Wang, K., Fu, A.WC., She, R., Pei, J. (2008). Privacy-Preserving Data Stream Classification. In: Aggarwal, C.C., Yu, P.S. (eds) Privacy-Preserving Data Mining. Advances in Database Systems, vol 34. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-70992-5_20
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DOI: https://doi.org/10.1007/978-0-387-70992-5_20
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