Achieving Both Valid and Secure Logistic Regression Analysis on Aggregated Data from Different Private Sources

Preserving the privacy of individual databases when carrying out statistical calculations has a long history in statistics and had been the focus of much recent attention in machine learning In this paper, the authors present a protocol for computing logistic regression when the data are held by separate parties without actually combining information sources by exploiting results from the literature on multi-party secure computation. They provide only the final result of the calculation compared with other methods that share intermediate values and thus present an opportunity for compromise of values in the combined database.

Provided by: Carnegie Mellon University Topic: Security Date Added: Dec 2011 Format: PDF

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