Secure ensemble training and inference using heterogeneous private machine learning models

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United States of America Patent

PATENT NO 11861476
APP PUB NO 20230058219A1
SERIAL NO

17405582

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Abstract

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One embodiment provides a method, including: receiving a query from a user; providing the query to data owners, wherein each of the data owners has a local machine learning model and wherein the plurality of data owners train a meta-model; secret sharing model output from the data owners between the other data owners, wherein the model output comprises an output responsive to the query computed using the local machine learning model; receiving, from each of the plurality of data owners, a set of meta-features corresponding to the query; and generating a response to the query, wherein the generating comprises determining, by evaluating the meta-model using the set of meta-features received from each of the plurality of data owners, weights for outputs from the local machine learning models and aggregating the outputs in view of the weights.

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Patent Owner(s)

Patent OwnerAddress
INTERNATIONAL BUSINESS MACHINES CORPORATIONNEW ORCHARD ROAD ARMONK NY 10504

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Inventor(s)

Inventor Name Address # of filed Patents Total Citations
Chaudhari, Harsh Mumbai, IN 1 0
Chemmengath, Saneem Ahmed Bangalore, IN 10 1
Dayama, Pankaj Satyanarayan Bangalore, IN 20 4
Nishad, Sandeep Lucknow, IN 2 0
Vinayagamurthy, Dhinakaran Erode, IN 11 3

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