SPAD – A Secure and Privacy-Preserving Distributed Analytics Framework

Conventional secure multi-party computation schemes depend on trusted parties and have high complexity, transparency, and data integrity concerns. This research presents SPAD, a secure, distributed, and privacy-preserving data storage and analytics framework designed to address the above challenges. SPAD leverages Shamir’s secret-sharing scheme, Paillier Homomorphic Encryption, and Distributed Ledger Technology to ensure the confidentiality and integrity of data, security of private keys, low communication overheads, transparency and better scalability. It also facilitates building a verifiable authentic dataset to augment accurate analytics. The security analysis determines how SPAD mitigates the threats of privacy leakage and malicious collusion and facilitates privacypreserving analytics. The experimental results, comprising execution time and memory usage, complement the efficient performance of the framework.