Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.04082 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908939211243520 |
|---|---|
| author | Zhao, Shixuan Wang, Weicheng Li, Ninghui Lin, Zhiqiang |
| author_facet | Zhao, Shixuan Wang, Weicheng Li, Ninghui Lin, Zhiqiang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_04082 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Styx: Collaborative and Private Data Processing With TEE-Enforced Sticky Policy Zhao, Shixuan Wang, Weicheng Li, Ninghui Lin, Zhiqiang Cryptography and Security 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. |
| title | Styx: Collaborative and Private Data Processing With TEE-Enforced Sticky Policy |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2604.04082 |