A Systematic Literature Review on How to Improve the Privacy of Artificial Intelligence Using Blockchain

Authors

Sebastian Duda, Branch Business & Information Systems Engineering of the Fraunhofer FITFollow
Dorian Geyer, University of BayreuthFollow
Tobias Guggenberger, Branch Business & Information Systems Engineering of the Fraunhofer FITFollow
Marc Principato, Branch Business & Information Systems Engineering of the Fraunhofer FITFollow
Dominik Protschky, Branch Business & Information Systems Engineering of the Fraunhofer FITFollow

Paper Number

1616

Abstract

Artificial Intelligence applications rely on large amounts of data. These Artificial Intelligence applications often also process personal data, leading to privacy problems. At the same time, the regulations regarding the use of data and privacy are getting stricter (e.g., the Personal Information Protection Law). Therefore, in this work, we investigate how Blockchain could help to improve the privacy of Artificial Intelligence applications. We conducted a systematic literature review to analyze existing approaches in the literature and abstracted them into categories. We identify federated learning in combination with Trained Model Sharing as the most popular approach. Additionally, we find that cryptographic methods usually complement most approaches, and that central collection and storage of raw data is not an option for any approach. Our work may serve as a foundation for developing a modular kit for privacy-preserving Blockchain-AI-systems

Comments

Paper Number 1616

Recommended Citation

Duda, Sebastian; Geyer, Dorian; Guggenberger, Tobias; Principato, Marc; and Protschky, Dominik, "A Systematic Literature Review on How to Improve the Privacy of Artificial Intelligence Using Blockchain" (2022). PACIS 2022 Proceedings. 176.
https://aisel.aisnet.org/pacis2022/176