Styx: Collaborative and Private Data Processing With TEE-Enforced Sticky Policy

• URL: http://arxiv.org/abs/2604.04082v1
• Date: Sun, 05 Apr 2026 12:00:51 GMT
• Title: Styx: Collaborative and Private Data Processing With TEE-Enforced Sticky Policy
• Abstract summary: This paper presents Styx, a novel framework to address sticky policies with Trusted Execution Environments (TEEs)<n>At a high level, Styx employs a hardware-TEE-protected with a programming language to form a sandboxed environment for both the data processing and policy enforcement.<n>We demonstrate its ability to make collaborative computing, such as joint AI training, more secure, privacy-preserving, and policy-compliant.
• Score: 14.732937051473606
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 joint AI training, more secure, privacy-preserving, and policy-compliant. Our evaluation shows the performance overheads imposed by Styx are reasonable on single-node computation with the capability to scale to a large distributed multi-node deployment.

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