A new scheme on privacy-preserving data classification

We address privacy-preserving classification problem in a distributed system. Randomization has been the approach proposed to preserve privacy in such scenario. However, this approach is now proven to be insecure as it has been discovered that some privacy intrusion techniques can be used to reconstruct private information from the randomized data tuples. We introduce an algebraic-technique-based scheme. Compared to the randomization approach, our new scheme can build classifiers more accurately but disclose less private information. Furthermore, our new scheme can be readily integrated as a middleware with existing systems.

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