Abstract
Classical data mining algorithms implicitly assume complete access to all data, either in centralized or federated form. However, privacy and security concerns often prevent sharing of data, thus derailing data mining projects. Recently, there has been growing focus on finding solutions to this problem. Several algorithms have been proposed that do distributed knowledge discovery, while providing guarantees on the non-disclosure of data. Classification is an important data mining problem applicable in many diverse domains. The goal of classification is to build a model which can predict an attribute (binary attribute in this work) based on the rest of attributes. We propose an efficient and secure privacy-preserving algorithm for support vector machine (SVM) classification over vertically partitioned data.
This research was supported in part by a Faculty Research Grant from Rutgers Business School – Newark and New Brunswick.
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Yu, H., Vaidya, J., Jiang, X. (2006). Privacy-Preserving SVM Classification on Vertically Partitioned Data. In: Ng, WK., Kitsuregawa, M., Li, J., Chang, K. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2006. Lecture Notes in Computer Science(), vol 3918. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11731139_74
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DOI: https://doi.org/10.1007/11731139_74
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