Immutable records based generation of machine learning models for dynamically triggering actions

Abstract

Machine Learning (ML) models are deployed in digital platforms for data analytics. However, it is realized that there is growing trends of recognition that machine learning models expose new vulnerabilities in software systems, for instance training data poisoning, adversarial responses, model extraction, and the like. Embodiments of the present disclosure provide systems and methods for safeguarding training dataset by exploiting immutability feature and generating immutable machine learning models for data analytics. More specifically, immutable records of events are governed by smart contracts within highly secure permissioned distributed ledger. This dataset is used for training multiple machine learning models which are immutable in nature and further utilized for triggering actions for incoming request(s) from IoT platforms.