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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.19360 |
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| _version_ | 1866915917138493440 |
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| author | Kokash, Natallia Belloum, Adam Grosso, Paola |
| author_facet | Kokash, Natallia Belloum, Adam Grosso, Paola |
| contents | Federated data processing (FDP) offers a promising approach for enabling collaborative analysis of sensitive data without centralizing raw datasets. However, real-world adoption remains limited due to the complexity of managing heterogeneous access policies, regulatory requirements, and long-running workflows across organizational boundaries. In this paper, we present a framework for compliance-aware FDP that integrates policy-as-code, workflow orchestration, and large language model (LLM)-assisted compliance management. Through the implemented prototype, we show how legal and organizational requirements can be collected and translated into machine-actionable policies in FDP networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_19360 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Compliance Management for Federated Data Processing Kokash, Natallia Belloum, Adam Grosso, Paola Software Engineering Federated data processing (FDP) offers a promising approach for enabling collaborative analysis of sensitive data without centralizing raw datasets. However, real-world adoption remains limited due to the complexity of managing heterogeneous access policies, regulatory requirements, and long-running workflows across organizational boundaries. In this paper, we present a framework for compliance-aware FDP that integrates policy-as-code, workflow orchestration, and large language model (LLM)-assisted compliance management. Through the implemented prototype, we show how legal and organizational requirements can be collected and translated into machine-actionable policies in FDP networks. |
| title | Compliance Management for Federated Data Processing |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2602.19360 |