标题： SynthGuard：通过可扩展且隐私保护的工作流框架重新定义合成数据生成 显示英文标题

标题： SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework

摘要： 数据驱动的应用在医疗、金融和执法等领域的日益依赖凸显了对数据生成和共享的安全、隐私保护和可扩展机制的需求。 合成数据生成（SDG）已成为一种有前景的方法，但通常依赖于集中式或外部处理，引发了关于数据主权、领域所有权和符合不断变化的监管标准的担忧。 为解决这些问题，我们引入了SynthGuard，这是一个旨在通过使数据所有者能够控制SDG工作流来确保计算治理的框架。 SynthGuard支持模块化和隐私保护的工作流，确保在各种环境中的安全、可审计和可重复执行。 在本文中，我们展示了SynthGuard如何通过与数据主权和监管合规性要求相一致，解决领域特定需求和可扩展SDG之间的复杂性。 SynthGuard通过与领域专家的输入进行迭代开发，并通过实际用例进行了验证，证明了其在确保合规性的同时平衡安全性、隐私性和可扩展性的能力。 评估证实了其在实施和执行SDG工作流以及在各种计算环境中集成隐私和效用评估方面的有效性。 显示英文摘要

摘要： The growing reliance on data-driven applications in sectors such as healthcare, finance, and law enforcement underscores the need for secure, privacy-preserving, and scalable mechanisms for data generation and sharing. Synthetic data generation (SDG) has emerged as a promising approach but often relies on centralized or external processing, raising concerns about data sovereignty, domain ownership, and compliance with evolving regulatory standards. To overcome these issues, we introduce SynthGuard, a framework designed to ensure computational governance by enabling data owners to maintain control over SDG workflows. SynthGuard supports modular and privacy-preserving workflows, ensuring secure, auditable, and reproducible execution across diverse environments. In this paper, we demonstrate how SynthGuard addresses the complexities at the intersection of domain-specific needs and scalable SDG by aligning with requirements for data sovereignty and regulatory compliance. Developed iteratively with domain expert input, SynthGuard has been validated through real-world use cases, demonstrating its ability to balance security, privacy, and scalability while ensuring compliance. The evaluation confirms its effectiveness in implementing and executing SDG workflows and integrating privacy and utility assessments across various computational environments.