Learning on graphs is becoming prevalent in a wide range of applications including social networks, robotics, communication, medicine, etc. These datasets belonging to entities often contain critical private information. The utilization of data for graph learning applications is hampered by the growing privacy concerns from users on data sharing. Existing privacy-preserving methods pre-process the data to extract user-side features, and only these features are used for subsequent learning. Unfortunately, these methods are vulnerable to adversarial attacks to infer private attributes. We present a novel privacy-respecting framework for distributed graph learning and graph-based machine learning. In order to perform graph learning and other downstream tasks on the server side, this framework aims to learn features as well as distances without requiring actual features while preserving the original structural properties of the raw data. The proposed framework is quite generic and highly adaptable. We demonstrate the utility of the Euclidean space, but it can be applied with any existing method of distance approximation and graph learning for the relevant spaces. Through extensive experimentation on both synthetic and real datasets, we demonstrate the efficacy of the framework in terms of comparing the results obtained without data sharing to those obtained with data sharing as a benchmark. This is, to our knowledge, the first privacy-preserving distributed graph learning framework.

翻译：图学习在包括社交网络、机器人技术、通信、医学等广泛的应用中日益普及。这些归属于实体的数据集通常包含关键的隐私信息。数据在图学习应用中的利用受到用户对数据共享日益增长的隐私担忧的阻碍。现有的隐私保护方法对数据进行预处理以提取用户侧特征，仅使用这些特征进行后续学习。不幸的是，这些方法容易受到推断隐私属性的对抗性攻击。我们提出了一种新颖的隐私尊重框架，用于分布式图学习和基于图的机器学习。为了在服务器端执行图学习及其他下游任务，该框架旨在学习特征和距离，而无需实际的原始特征，同时保留原始数据的结构属性。所提出的框架具有高度的通用性和适应性。我们展示了欧几里得空间的有效性，但它可以应用于任何现有的距离近似和图学习方法，适用于相关空间。通过对合成数据集和真实数据集的大量实验，我们以不共享数据所获得的结果与共享数据的结果作为基准进行比较，证明了该框架的有效性。据我们所知，这是首个隐私保护的分布式图学习框架。