DPCapsule: A Decentralized Private Computing System with Self-Controlled Data.

Machine learning and data analysis algorithms leverage massive datasets to deliver powerful functionalities. However, these datasets are often distributed across multiple parties and individual users. Decentralized private computing systems enable data consumers to execute algorithms on third-party data in a secure manner, eliminating the need for trust in any single node. Nevertheless, existing systems, primarily derived from blockchain technology, target small-scale workloads. Sharing large-scale datasets presents new challenges. First is data-oriented access control, enabling data providers to enforce flexible access policies throughout the complex utilization of their datasets. Second is high performance, which is essential for data analysis applications involving large-scale datasets and sophisticated computational logic. Third is whole-lifecycle privacy, ensuring comprehensive protection of both data and algorithmic privacy from task initiation through result delivery. To address these challenges, we present DPCapsule, a high-performance decentralized computing system that maintains whole-lifecycle privacy. DPCapsule introduces a novel data abstraction called Capsule, which encapsulates dataset and access policies within a trusted execution environment (TEE)-based shell, enabling data providers to control their datasets throughout subsequent computations. A Capsule reborn mechanism is provided for automated access policy updates. Additionally, we design a two-layer execution architecture and consensus protocol to facilitate private computation with scalable performance. Furthermore, a secure execution protocol is designed to guarantee whole-lifecycle privacy for both dataset, algorithms, and metadata. We have developed a prototype of DPCapsule and conducted evaluations across various configurations, with networks scaling up to 32 nodes. Experimental results show that DPCapsule effectively scales to 32 nodes, achieving 116x and 2.2 * 10(7) x latency speedup for database and machine learning applications, respectively, compared with Ethereum.