Project Falcon

Falcon (A Federated Learning Platform with Privacy Protection)

Falcon is designed for federated learning with privacy protection, allowing multiple parties to train a model on their joint data and make predictions without disclosing their private data, so as to abide by the privacy regulations.

Falcon focuses on cross-silo data collaboration. It combines homomorphic encryption, secure multiparty computation, and differential privacy to ensure data security and privacy.

View our Falcon publication for International Conference on Very Large Data Bases or VLDB 2020

Privacy Preserving Vertical Federated Learning for Tree-based Models.

Research Highlights

Finance Example

Examplary Application A motivating example based on the our fintech could be in a car insurance application. For example, the insurance company can use AI, federated learning to conduct personalized analytics of car owners, according to their car information, driving habits, financial condition, etc., and provide a customized car insurance price for them. Meanwhile, the insurance information can be stored and maintained on a blockchain, and they can use smart contracts to automatically complete the payment and insurance claim, making the whole transaction process more efficient, secure, and reliable.

Falcon Overview

Project Falcon (A Federated Learning Platform with Privacy Protection): Falcon is designed for federated learning with privacy protection, allowing multiple parties to train a model on their joint data and make predictions without disclosing their private data, so as to abide by the privacy regulations.

People

Professors

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Ooi Beng Chin

Distinguished Professor, Computer Science, School of Computing

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Xiaokui Xiao

Associate Professor Computer Science, School of Computing, NUS

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Gang Chen

Professor, Dean of computer college, Zhejiang University

Researchers

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Yuncheng Wu

Research Fellow in the Database Systems Research Group at NUS

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Shaofeng Cai

PhD student, NUS School of Computing

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