Microsoft Technology Licensing, LLC

Ryan T Tecco

Ryan M Rogers

Jing Wang

Man Chun D Leung

Parvez Ahammad

Manoj Rameshchandra Thakur

David Pardoe

Rahul Tandra

Bing Liu

Yajun Wang

Saket Kumar

Sudhanshu Arora

Devang N Jhaveri

Shawn F Ren

Liangzhong Yin

Technologies for secure multi-party computation include computing first double-encrypted data, computing second double-encrypted data, and, in a trusted execution environment, executing a query on the first double-encrypted data and the second double encrypted data to create a query-processed double-encrypted data set. The trusted execution environment can provide the query-processed double-encrypted data set to a requester such as another computer, system, or process.

Secure multi-party computation

Embodiments of the disclosed technologies receive first event data associated with a first party application, receive second event data representing a click, in the first party application, on a link to a third party application, receive third event data from the third party application, convert the third event data to a label, map a compressed format of the labeled third event data to the first event data and the second event data to create multi-party attribution data, group multiple instances of the multi-party attribution data into a batch, add noise to the compressed format of the labeled third event data in the batch, and send the noisy batch to a second computing device. A debiasing algorithm can be applied to the noisy batch. The debiased noisy batch can be used to train at least one machine learning model.

On-device privatization of multi-party attribution data

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Philadelphia, PA, United States of America

Ryan T Tecco