Graph data is increasingly prevalent across domains, offering analytical value but raising significant privacy concerns. Edges may encode sensitive relationships, while node attributes may contain sensitive entity or personal data. Differential Privacy (DP) has gained traction for its strong guarantees, yet applying DP to graphs is challenging because of their complex relational structure, leading to trade-offs between privacy and utility. Existing methods vary in privacy definitions, utility goals, and contextual settings, complicating comparison. For practitioners, this is compounded by DP's interpretability issues, contributing to misleading protection claims. To address this, we propose a novel systemisation of existing methods tailored to practical considerations and adaptable to varying practitioner objectives. Our contributions include: (i) a comprehensive survey of differentially private graph release methods; (ii) identification of key vulnerabilities; and (iii) a practitioner-oriented, objective-based framework to guide the selection, interpretation, and sound evaluation of existing methods. We demonstrate the use of our systemisation through two exemplary scenarios in which we assume the role of a social network analyst, apply it, and conduct evaluations in accordance with our framework. Together, these two illustrative instantiations ultimately provide a unified benchmark for state-of-the-art methods in the social networks domain.

翻译：图数据在各个领域日益普遍，在提供分析价值的同时也引发了显著的隐私问题。边可能编码敏感关系，节点属性则可能包含敏感实体或个人数据。差分隐私（DP）因其强有力的保证而受到广泛关注，然而，由于图复杂的关联结构，将DP应用于图十分具有挑战性，这导致了隐私与效用之间的权衡。现有方法在隐私定义、效用目标和上下文设置上各不相同，使得比较变得复杂。对于实践者而言，DP的可解释性问题又加剧了这一困境，导致了具有误导性的保护声明。为解决这个问题，我们提出了一种针对实践考量且可适应不同实践者目标的新颖的现有方法系统化分类。我们的贡献包括：(i) 对差分隐私图发布方法的全面综述；(ii) 识别关键脆弱性；(iii) 一个面向实践者、基于目标的框架，用以指导现有方法的选择、解读和合理评估。我们通过两个示例场景展示了我们系统化分类的应用，在这些场景中，我们假设自己扮演社交网络分析者的角色，应用该分类，并根据我们的框架进行评估。这两个说明性实例共同为社交网络领域的最先进方法提供了一个统一基准。