On the Suitability of Applying WordNet to Privacy Measurement

Zhu, Nafei; Wang, Siyu; He, Jingsha; Teng, Da; Peng, Huan; Zhang, Yuqiang

doi:10.1007/s11277-018-5447-5

Cited by 3 publications

(1 citation statement)

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“…Intuitively, the higher the semantic similarity between two concepts, the higher the probability of information leakage of one concept when the other concept is leaked. However, Zhu et al found that the correlation between calculation results and scores obtained through questionnaires was somewhat low [27], implying that it was not enough to describe the association and transmission probability of privacy information by only considering the similarity of words in the WordNet.…”

Section: Figure 1 Example Of the Hierarchical Structurementioning

confidence: 99%

A Semantic Inference Based Method for Privacy Measurement

Chen

Zhu

et al. 2020

IEEE Access

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In the era of Internet and big data, an increasing number of intelligent applications have been developed. As the result, a lot of user data can be collected and stored by Internet companies as well as by ordinary users through various media platforms such as Facebook, WeChat, etc. that may contain information related to personal privacy. Even though privacy protection has been declared by Internet service providers, after collecting enough amount of seemingly less relevant data, an attacker can still infer user privacy via one means or another, e.g., by running a data mining algorithm. This can undoubtedly bring high risk of privacy disclosure to users under such an attack model. So, accurately measuring the leakage of privacy becomes an urgent issue. Although many privacy measurement and protection methods have been proposed in recent years, they mainly target at structured datasets and are thus inadequate to the measurement of the disclosure of specific privacy information. In addition, most of the methods have failed to consider the internal connections and relationships between privacy information and thus cannot be used to measure the implicit privacy disclosure risk on unstructured data. In this paper, we propose a semantic inference method based on the WordNet ontology to measure privacy disclosure in which we employ an information content (IC) based method to determine the weight of attributes to describe the inference preferences in the process of inferring privacy. Experiment was performed to verify the effectiveness of the IC based inference weight assignment method and to compare the proposed measurement method to some privacy disclosure behavior learned through a data mining algorithm and some existing privacy measurement methods to demonstrate the advantages of the proposed method for measuring privacy disclosure.

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Section: Figure 1 Example Of the Hierarchical Structurementioning

confidence: 99%