Blockchain-Supported Intelligent Systems for Privacy-Preserving Data Analytics

Abstract

The rapid growth of intelligent systems and data-driven technologies has increased the demand for secure and privacy-preserving data analytics. Organizations increasingly rely on machine learning and artificial intelligence to extract insights from massive datasets, yet the sensitive nature of modern data—such as healthcare records, financial transactions, and personal behavioral information—creates significant privacy challenges. Traditional centralized analytics frameworks often require data sharing among multiple entities, which increases risks of unauthorized access, data leakage, and privacy violations. Blockchain technology has emerged as a promising solution to these challenges due to its decentralized architecture, immutable ledger, and cryptographic security mechanisms. This research investigates a blockchain-supported intelligent system framework designed to enable privacy-preserving data analytics across distributed environments. The proposed architecture integrates blockchain infrastructure with advanced privacy-preserving techniques such as federated learning, homomorphic encryption, and differential privacy to allow collaborative analytics without exposing raw data. Blockchain acts as a secure coordination layer that records transactions, ensures data integrity, and enforces trust among participants.