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Title: K-Nearest Neighbor Classification Using Anatomized Data

Citation Type: Miscellaneous

Publication Year: 2016

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Abstract: This paper analyzes k nearest neighbor classification with training data anonymized using anatomy. Anatomy preserves all data values, but introduces uncertainty in the mapping between identifying and sensitive values. We first study the theoretical effect of the anatomized training data on the k nearest neighbor error rate bounds, nearest neighbor convergence rate, and Bayesian error. We then validate the derived bounds empirically. We show that 1) Learning from anatomized data approaches the limits of learning through the unprotected data (although requiring larger training data), and 2) nearest neighbor using anatomized data outperforms nearest neighbor on generalization-based anonymization.

Url: https://arxiv.org/pdf/1610.06048.pdf

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Authors: Mancuhan, Koray; Clifton, Chris

Publisher: Cornell University Library arXiv

Data Collections: IPUMS USA

Topics: Other

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