Privacy protection has received a lot of attention in recent years since in the era of big data, abundant information about individuals can be easily acquired. Meanwhile, as a prerequisite for effective privacy protection, the measurement of privacy disclosure is essential. Although some work has been done on the evaluation of privacy disclosure via quantification for the protection of privacy, not much attention has been placed on exploring the relationships between privacy information, resulting in underestimation, if not ill-formed reasoning, of privacy disclosure. In this paper, we propose an ontology-based approach to measure privacy disclosure by exploring the relationships between privacy information based on the WordNet. We first propose an algorithm for deriving or measuring privacy disclosure based on a set of words or concepts from text data related to individuals to ensure that the disclosure of certain user privacy can still be deduced and measured even if the set of words or concepts don’t seem to be much related to it. We then perform a set of experiment by applying the proposed algorithm to some public information of ten public figures from different walks of life to evaluate the effectiveness of the algorithm and to shed some light on the characteristics of privacy disclosure in the real world in the era of big data. The work can thus serve as the foundation for the development of mechanisms for limiting or reducing privacy disclosure to achieve better protection of individual privacy.

近年来，随着大数据时代的到来，大量的个人信息可以轻松获取，隐私保护备受关注。同时，作为有效保护隐私的前提，隐私披露的衡量也是必不可少的。尽管通过量化来评估隐私披露以保护隐私已经做了一些工作，但没有太多关注探索隐私信息之间的关系，导致对隐私披露的低估，如果不是错误的推理。在本文中，我们通过探索基于 WordNet 的隐私信息之间的关系，提出了一种基于本体的方法来衡量隐私披露。我们首先提出了一种基于一组与个人相关的文本数据中的词或概念来推导或衡量隐私披露的算法，以确保即使该词或概念集不存在，仍然可以推导出和衡量某些用户隐私的披露。似乎与它有很大关系。然后我们通过将所提出的算法应用到来自不同行业的十位公众人物的一些公开信息进行了一组实验，以评估该算法的有效性，并揭示该时代现实世界中隐私泄露的特征。的大数据。因此，这项工作可以作为开发限制或减少隐私披露机制的基础，以更好地保护个人隐私。