With the development of Internet technology, service providers can provide users with personalized services to enrich user experience, however, this often requires a large number of users’ private data. Meanwhile, the protection of their private data and the evaluation of the risk of leaked datasets become a matter of great concern to many people. To resolve these issues, in this paper, we develop a machine learning-based approach in online social networks (OSNs) to efficiently correlate the leaked datasets and accurately learn millions of users’ confidential information. Moreover, a trust evaluation model is developed in OSNs to identify malicious service providers and secure users’ social activities via direct trust computing and indirect trust computing. Extensive experiments are conducted by using real-world leaked datasets, and the results show that the efficiency and effectiveness of the proposed approach in terms of user privacy protection and accuracy of privacy leakage evaluation.

随着Internet技术的发展，服务提供商可以为用户提供个性化服务，以丰富用户体验，但是，这通常需要大量用户的私有数据。同时，保护其私人数据和评估数据集泄漏的风险已成为许多人关注的问题。为了解决这些问题，在本文中，我们在在线社交网络（OSN）中开发了一种基于机器学习的方法，以有效地关联泄漏的数据集并准确地学习数百万个用户的机密信息。此外，在OSN中开发了一种信任评估模型，以通过直接信任计算和间接信任计算来识别恶意服务提供商并保护用户的社交活动。通过使用真实的泄漏数据集进行了广泛的实验，