The application of graph analytics to various domains has yielded tremendous societal and economical benefits in recent years. However, the increasingly widespread adoption of graph analytics comes with a commensurate increase in the need to protect private information in graph data, especially in light of the many privacy breaches in real-world graph data that were supposed to preserve sensitive information. This article provides a comprehensive survey of private graph data release algorithms that seek to achieve the fine balance between privacy and utility, with a specific focus on provably private mechanisms. Many of these mechanisms are natural extensions of the Differential Privacy framework to graph data, but we also investigate more general privacy formulations like Pufferfish Privacy that address some of the limitations of Differential Privacy. We also provide a wide-ranging survey of the applications of private graph data release mechanisms to social networks, finance, supply chain, and health care. This article should benefit practitioners and researchers alike in the increasingly important area of private analytics and data release.

近年来，图分析在各个领域的应用产生了巨大的社会和经济效益。然而，随着图形分析的日益广泛采用，保护图形数据中私人信息的需求也相应增加，特别是考虑到现实世界图形数据中本应保护敏感信息的许多隐私泄露事件。本文对旨在实现隐私和实用性之间良好平衡的私有图数据发布算法进行了全面调查，并特别关注可证明的私有机制。其中许多机制是差分隐私框架对图形数据的自然扩展，但我们也研究了更通用的隐私公式，如 Pufferfish Privacy，它解决了差分隐私的一些局限性。我们还对私有图谱数据发布机制在社交网络、金融、供应链和医疗保健领域的应用进行了广泛的调查。这篇文章应该有益于日益重要的私人分析和数据发布领域的从业者和研究人员。