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

2026-04-05

AI Summary

This work addresses the challenge of jointly training AI models across mutually untrusting parties while simultaneously protecting sensitive data and enforcing heterogeneous policy requirements. To this end, the authors propose a middleware framework leveraging Trusted Execution Environments (TEEs), which uniquely integrates sticky policies with TEEs to enable dynamic, fine-grained mandatory access control throughout the entire data lifecycle—including usage, derivation, and transfer. The framework achieves this through policy-driven runtime sandboxing, secure data processing pipelines, and hardware-enforced isolation. Experimental results demonstrate that the system supports efficient collaborative training with strong privacy guarantees, incurs manageable performance overhead on a single node, and scales effectively in distributed settings.

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.

Problem

• privacy
• data collaboration
• sticky policy
• Trusted Execution Environment
• data protection

Innovation

• Trusted Execution Environment
• Sticky Policy
• Data Lifecycle Protection
• Privacy-Preserving Collaboration
• Policy Enforcement