Analysis of Models for Decentralized and Collaborative AI on Blockchain

Machine learning has recently enabled large advances in artificial intelligence, but these results can be highly centralized. The large datasets required are generally proprietary; predictions are often sold on a per-query basis; and published models can quickly become out of date without effort to acquire more data and maintain them. Published proposals to provide models and data for free for certain tasks include Microsoft Research's Decentralized and Collaborative AI on Blockchain. The framework allows participants to collaboratively build a dataset and use smart contracts to share a continuously updated model on a public blockchain. The initial proposal gave an overview of the framework omitting many details of the models used and the incentive mechanisms in real world scenarios. In this work, we evaluate the use of several models and configurations in order to propose best practices when using the Self-Assessment incentive mechanism so that models can remain accurate and well-intended participants that submit correct data have the chance to profit. We have analyzed simulations for each of three models: Perceptron, Na\"ive Bayes, and a Nearest Centroid Classifier, with three different datasets: predicting a sport with user activity from Endomondo, sentiment analysis on movie reviews from IMDB, and determining if a news article is fake. We compare several factors for each dataset when models are hosted in smart contracts on a public blockchain: their accuracy over time, balances of a good and bad user, and transaction costs (or gas) for deploying, updating, collecting refunds, and collecting rewards. A free and open source implementation for the Ethereum blockchain and simulations written in Python is provided at https://github.com/microsoft/0xDeCA10B. This version has updated gas costs using newer optimizations written after the original publication.Comment: Accepted to ICBC 202

Assessing Blockchain’s Potential to Ensure Data Integrity and Security for AI and Machine Learning Applications

The increasing use of data-centric approaches in the fields of Machine Learning and Artificial Intelligence (ML/AI) has raised substantial issues over the security, integrity, and trustworthiness of data. In response to this challenge, Blockchain technology offered a promising and practical solution, as its inherent characteristics as a decentralized distributed ledger, coupled with cryptographic processes, offer an unprecedented level of data confidentiality and immutability. This study examines the mutually beneficial connection between Blockchain technology and ML/AI, using Blockchain\u27s inherent capacity to protect against unauthorized alterations of data during the training phase of ML models. The method involves building valid blocks of data from the training dataset and then sending them to the mining process using smart contracts and the Proof of Work (PoW) consensus method. Using SHA256 to produce a cryptographic signature for each data block improves the aforementioned procedure. The public Ethereum blockchain serves as a secure repository for these signatures, whereas a cloud-based infrastructure houses the original data file. Particularly during the training phase of Machine Learning (ML) models, this cryptographic framework is critical in ensuring the data verification procedure. This research investigates the potential collaboration between Blockchain technology and ML/AI, bolstering data quality and trust to enhance data-driven decision-making fortifying the models\u27 ability to provide precise and dependable results