A Platform for Privacy-preserving Machine Learning Using the Ethereum Blockchain and Smart Contracts

Reference number
Coordinator Scaleout Systems AB
Funding from Vinnova SEK 1 700 000
Project duration August 2019 - September 2020
Status Ongoing
Venture Collaboration projects in cybersecurity and digital infrastructure
Call Cybersecurity and reliability - Theme: Identity and block chains

Purpose and goal

AI faces two major challenges. One is that in most industries data exists in isolation. The other is the strengthening of data privacy and security. We propose a solution to these challenges: a collaborative federated machine learning platform. Specifically, we will use blockchain to create an immutable audit trail enabling trust and transparency, privacy-preserving encryption enhancements in communication, and demonstrate the use of smart contracts for automation and governance.

Expected results and effects

Our solution will contribute to increased trust in the digital society by allowing advanced machine learning to be developed on distributed data with full respect of the confidentiality of the data providers and the property rights of the companies that propose the machine learning models. It will create opportunities for both small and large scale organisations to work together and securely build highly accurate models that was not possible before due to the strict data sharing policies.

Planned approach and implementation

The project is divided into three phases. Preparation, Development and Demonstration. Preparation Phase: Work package 1 for project establishing activities. Development Phase: Work packages 2 to 5 spans creating user scenarios and detailed use cases to iterative MVP work. Each development iteration consists of client application, smart contract and infrastructure development and operations as well as ref group demo and MVP evaluation. Demonstration Phase: Work package 6 - Seminars and presentations to relay the results.

External links

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 28 June 2019

Reference number 2019-02819

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