公开(公告)号：US20240143416A1

公开(公告)日：2024-05-02

申请号：US17978933

申请日：2022-11-01

Applicant: Microsoft Technology Licensing, LLC

Inventor： Ryan M. Rogers ,  Man Chun D. Leung ,  David Pardoe ,  Bing Liu ,  Shawn F. Ren ,  Rahul Tandra ,  Parvez Ahammad ,  Jing Wang ,  Ryan T. Tecco ,  Yajun Wang

IPC: G06F9/54 ,  G06F21/64

CPC classification number: G06F9/54 ,  G06F21/645

Abstract: 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.

公开(公告)号：US09710493B2

公开(公告)日：2017-07-18

申请号：US13791666

申请日：2013-03-08

Applicant: Microsoft Technology Licensing, LLC

Inventor： Jingdong Wang ,  Qifa Ke ,  Shipeng Li ,  Jing Wang

IPC: G06F17/30

CPC classification number: G06F17/30256 ,  G06F17/30705

Abstract: A set of data points is divided into a plurality of subsets of data points. A set of cluster closures is generated based at least in part on the subset of data points. Each cluster closure envelopes a corresponding cluster of a set of clusters and is comprised of data points of the enveloped cluster and data points neighboring the enveloped cluster. A k-Means approximator iteratively assigns data points to a cluster of the set of clusters and updates a set of cluster centroids corresponding to the set of clusters. The k-Means approximator assigns data points based at least in part on the set of cluster closures.

公开(公告)号：US20240169074A1

公开(公告)日：2024-05-23

申请号：US17993661

申请日：2022-11-23

Applicant: Microsoft Technology Licensing, LLC

Inventor： Man Chun D. Leung ,  Saket Kumar ,  Ryan T. Tecco ,  Jing Wang ,  Ryan M. Rogers ,  Manoj R. Thakur ,  Devang N. Jhaveri ,  Sudhanshu Arora ,  Liangzhong Yin

IPC: G06F21/60 ,  G06F21/53

CPC classification number: G06F21/602 ,  G06F21/53 ,  G06F2221/034

Abstract: 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.

公开(公告)号：US11792167B2

公开(公告)日：2023-10-17

申请号：US17219482

申请日：2021-03-31

Applicant: Microsoft Technology Licensing, LLC

Inventor： Haifeng Zhao ,  Yang Chen ,  Jiashuo Wang ,  Xiaojing Chen ,  Chencheng Wu ,  Souvik Ghosh ,  Ankit Gupta ,  Jing Wang ,  John Patrick Moore ,  Henry Heyburn Pistell ,  Mira Thambireddy ,  Haowen Cao ,  Keyi Yu

IPC: H04L9/40 ,  G06N20/00

CPC classification number: H04L63/0428 ,  G06N20/00

Abstract: Techniques for a flexible data security and machine learning system for merging third-party data are provided. In one technique, the system receives a data set from a third-party entity and receives selection data that indicates that the third-party entity selected a set of data security policies that includes an encryption option and a data mixing option from among multiple data mixing options. In response to receiving the selection data, the system stores data that associates the set of data security policies with the data set, encrypts the data set according to the encryption option, and persistently stores the encrypted data set. Later, the system decrypts the encrypted data set in volatile memory, generates, based on the data mixing option, training data based on the decrypted version of the data set, trains a machine-learned model based on the training data, and stores the machine-learned model in association with the data set.

公开(公告)号：US20230351247A1

公开(公告)日：2023-11-02

申请号：US17735020

申请日：2022-05-02

Applicant: Microsoft Technology Licensing, LLC

Inventor： Boyi Chen ,  Tong Zhou ,  Siyao Sun ,  Lijun Peng ,  Xinruo Jing ,  Vakwadi Thejaswini Holla ,  Yi Wu ,  Pankhuri Goyal ,  Souvik Ghosh ,  Zheng Li ,  Yi Zhang ,  Onkar A. Dalal ,  Jing Wang ,  Aarthi Jayaram

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Embodiments of the disclosed technologies receive a first-party trained model and a first-party data set from a first-party system into a protected environment, receive a first third-party data set into the protected environment, and, in a data clean room, joining the first-party data set and the first third-party data set to create a joint data set for the particular segment, tuning a first-party trained model with the joint data set to create a third-party tuned model, sending model parameter data learned in the data clean room as a result of the tuning to an aggregator node, receiving a globally tuned version of the first-party trained model from the aggregator node, applying the globally tuned version of the first-party trained model to a second third-party data set to produce a scored third-party data set, and providing the scored third-party data set to a content distribution service of the first-party system.