A Semantic Inference Based Method for Privacy Measurement
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9241695/ |
| _version_ | 1818446700781502464 |
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| author | Baocun Chen Nafei Zhu Jingsha He Peng He Shuting Jin Shijia Pan |
| author_facet | Baocun Chen Nafei Zhu Jingsha He Peng He Shuting Jin Shijia Pan |
| author_sort | Baocun Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T19:51:54Z |
| format | Article |
| id | doaj.art-5817e36cff5249738b5c44d2ee1620c2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T19:51:54Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-5817e36cff5249738b5c44d2ee1620c22022-12-21T22:49:23ZengIEEEIEEE Access2169-35362020-01-01820011220012810.1109/ACCESS.2020.30343989241695A Semantic Inference Based Method for Privacy MeasurementBaocun Chen0https://orcid.org/0000-0001-5846-3129Nafei Zhu1https://orcid.org/0000-0003-4036-0724Jingsha He2https://orcid.org/0000-0002-8122-8052Peng He3https://orcid.org/0000-0001-5360-8798Shuting Jin4Shijia Pan5Faculty of Information Technology, Beijing University of Technology, Beijing, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing, ChinaCollege of Computer and Information Technology, China Three Gorges University, Yichang, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing, ChinaIn 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.https://ieeexplore.ieee.org/document/9241695/Privacy disclosureprivacy inference weightprivacy quantificationsemantic inferencewordNet |
| spellingShingle | Baocun Chen Nafei Zhu Jingsha He Peng He Shuting Jin Shijia Pan A Semantic Inference Based Method for Privacy Measurement IEEE Access Privacy disclosure privacy inference weight privacy quantification semantic inference wordNet |
| title | A Semantic Inference Based Method for Privacy Measurement |
| title_full | A Semantic Inference Based Method for Privacy Measurement |
| title_fullStr | A Semantic Inference Based Method for Privacy Measurement |
| title_full_unstemmed | A Semantic Inference Based Method for Privacy Measurement |
| title_short | A Semantic Inference Based Method for Privacy Measurement |
| title_sort | semantic inference based method for privacy measurement |
| topic | Privacy disclosure privacy inference weight privacy quantification semantic inference wordNet |
| url | https://ieeexplore.ieee.org/document/9241695/ |
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