Invention Grant
- Patent Title: Distributed machine learning via secure multi-party computation and ensemble learning
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Application No.: US17363615Application Date: 2021-06-30
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Publication No.: US11265166B2Publication Date: 2022-03-01
- Inventor: Ramtin Mehdizadeh Seraj , Nicholas Chow
- Applicant: Dapper Labs Inc.
- Applicant Address: CA Vancouver
- Assignee: Dapper Labs Inc.
- Current Assignee: Dapper Labs Inc.
- Current Assignee Address: CA Vancouver
- Agency: Sheppard Mullin Richter & Hampton LLP
- Main IPC: H04L29/06
- IPC: H04L29/06 ; H04L9/32 ; G06N5/04 ; G06N20/00 ; G06F21/00 ; G06F21/51 ; H04L9/06

Abstract:
Systems and methods for combining input data and machine learning models that remain secret to each entity are described. This disclosure can allow groups of entities to compute predictions based on datasets that are larger and more detailed collectively than individually, without revealing their data to other parties. This is of particular use in artificial intelligence (AI) tasks in domains which deal with sensitive data, such as medical, financial, or cybersecurity.
Public/Granted literature
- US20210409220A1 DISTRIBUTED MACHINE LEARNING VIA SECURE MULTI-PARTY COMPUTATION AND ENSEMBLE LEARNING Public/Granted day:2021-12-30
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