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Machine learning algorithms learn from data and use data from databases thatare mutable; therefore, the data and the results of machine learning cannot befully trusted. Also, the machine learning process is often difficult toautomate. A unified analytical framework for trustable machine learning hasbeen presented in the literature. It proposed building a trustable machinelearning system by using blockchain technology, which can store data in apermanent and immutable way. In addition, smart contracts on blockchain areused to automate the machine learning process. In the proposed framework, acore machine learning algorithm can have three implementations: server layerimplementation, streaming layer implementation, and smart contractimplementation. However, there are still open questions. First, the streaminglayer usually deploys on edge devices and therefore has limited memory andcomputing power. How can we run machine learning on the streaming layer?Second, most data that are stored on blockchain are financial transactions, forwhich fraud detection is often needed. However, in some applications, trainingdata are hard to obtain. Can we build good machine learning models to do frauddetection with limited training data? These questions motivated this paper;which makes two contributions. First, it proposes training a machine learningmodel on the server layer and saving the model with a special binary dataformat. Then, the streaming layer can take this blob of binary data as inputand score incoming data online. The blob of binary data is very compact and canbe deployed on edge devices. Second, the paper presents a new method ofsynthetic data generation that can enrich the training data set. Experimentsshow that this synthetic data generation is very effective in applications suchas fraud detection in financial data.

Trustable and Automated Machine Learning Running with Blockchain and Its Applications