Microsoft Technology Licensing, LLC

Peter Byerley Rindal

Hao Chen

Kristin E Lauter

Kim Laine

Kim Henry Martin Laine

Zhicong Huang

Ran Gilad-Bachrach

Melissa Erin Chase

Yuhou Xia

Gizem Selcan Cetin

Systems and methods for computing a private set intersection are disclosed. A method includes storing, at a sender device, a first set of values. The method includes receiving, from a receiver device, a homomorphic encryption of a receiver device value. The method includes computing a homomorphically encrypted number based on a difference between the homomorphic encryption of the receiver device value and each value in the first set of values, and based on a hash function of the encryption of the receiver device value. The method includes transmitting the homomorphically encrypted number to the receiver device for determination, at the receiver device, whether the receiver device value is in the first set of values.

Computing a private set intersection

Techniques for using different data sources for a predictive model are described. According to various implementations, techniques described herein enable different data sets to be used to generate a predictive model, while minimizing the risk that individual data points of the data sets will be exposed by the predictive model. This aids in protecting individual privacy (e.g., protecting personally identifying information for individuals), while enabling robust predictive models to be generated using data sets from a variety of different sources.

Using different data sources for a predictive model

In aspects of string matching in encrypted data, a computing device stores homomorphic encrypted data as a dataset, and implements a string matching application that receives an encrypted query string as a query of the homomorphic encrypted data. The string matching application can then apply algorithms to perform addition and multiplication operations, and determine whether there are matching strings of the encrypted query string in the dataset. The string matching application can compute, for each row of the dataset, a sum of some function of dataset bits and query bits for a row result, and multiply the row results of the computed rows to determine matching strings. Alternatively, the string matching application can compute, for each row of the dataset, a product over some function of the dataset bits and the query bits for a row result, and add the row results of the computed rows to determine matching strings.

String matching in encrypted data

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Corvallis, OR, United States of America

Peter Byerley Rindal