Styx: Collaborative and Private Data Processing With TEE-Enforced Sticky Policy
Shixuan Zhao, Weicheng Wang, Ninghui Li, Zhiqiang Lin

TL;DR
Styx is a framework that combines sticky policies with Trusted Execution Environments to enable secure, privacy-preserving, and policy-compliant collaborative data processing and AI training.
Contribution
It introduces a novel TEE-based middleware with a programming language runtime for flexible, data-specific policy enforcement throughout data lifecycle and derivation.
Findings
Styx effectively enforces policies during data processing and collaboration.
The implementation demonstrates reasonable performance overheads.
Styx scales from single-node to large distributed deployments.
Abstract
Protecting sensitive information in data-driven collaborations, such as AI training, while meeting the diverse requirements of multiple mutually distrusted stakeholders, is both crucial and challenging. This paper presents Styx, a novel framework to address this challenge by integrating sticky policies with Trusted Execution Environments (TEEs). At a high level, Styx employs a hardware-TEE-protected middleware with a programming language runtime to form a sandboxed environment for both the data processing and policy enforcement. We carefully designed a data processing workflow and pipelines to enable a strong yet flexible data-specific policy enforcement throughout the entire data lifecycle and data derivation to achieve data-in-use protection, data lifecycle protection and dynamic collaboration. We implemented Styx and demonstrated its ability to make collaborative computing, such as…
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