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Main Authors: Kokash, Natallia, Belloum, Adam, Grosso, Paola
Format: Preprint
Published: 2026
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Online Access:https://arxiv.org/abs/2602.19360
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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