Decentralized and secure deduplication with dynamic ownership in MLaaS

Abstract

Machine Learning as a Service (MLaaS) these days has been an integral offering of many Internet giants, shaping people’s lives in an intelligent manner. Both academic and industrial communities are dedicated to exploring a variety of functional MLaaS platforms, where massive data is typically required for model training to achieve better capability. As the widely used training data can be repeatedly stored, data deduplication has been deemed essential. Meanwhile, the proliferation of ML-aimed attacks has raised security awareness. In light of these, we propose a secure and robust deduplication scheme over encrypted data for decentralized MLaaS platforms. We utilize message-locked encryption for privacy protection on blockchain over decentralized cloud storage. To balance the overhead from blockchain, we offload the computation off-chain and only maintain the system state on-chain. We remedy the potential leakages from the transparency of blockchain, through carefully tailored cross-user deduplication workflow. Our proposed scheme is also robust against short-information and brute-force attacks. Furthermore, we apply binary tree based key distribution to support dynamic ownership updates. We implement a prototype on Ethereum, and comprehensive experiments show that our design can function as intended with modest on-chain update gas cost (i.e., 1.67×10−4 ETH), and the blockchain-related operations run less than 6 s.