Ontology-Based Approach for the Measurement of Privacy Disclosure
Abstract 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 wo...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhu, Nafei [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Information systems frontiers - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1999, 24(2021), 5 vom: 17. Aug., Seite 1689-1707 |
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| Übergeordnetes Werk: |
volume:24 ; year:2021 ; number:5 ; day:17 ; month:08 ; pages:1689-1707 |
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| DOI / URN: |
10.1007/s10796-021-10180-2 |
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SPR04880620X |
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| 520 | |a Abstract 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. | ||
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| 700 | 1 | |a Wang, Siyu |4 aut | |
| 700 | 1 | |a Teng, Da |4 aut | |
| 700 | 1 | |a He, Jingsha |4 aut | |
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10.1007/s10796-021-10180-2 doi (DE-627)SPR04880620X (SPR)s10796-021-10180-2-e DE-627 ger DE-627 rakwb eng Zhu, Nafei verfasserin aut Ontology-Based Approach for the Measurement of Privacy Disclosure 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract 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. Privacy (dpeaa)DE-He213 Ontology (dpeaa)DE-He213 Privacy disclosure, privacy quantification (dpeaa)DE-He213 Chen, Baocun aut Wang, Siyu aut Teng, Da aut He, Jingsha aut Enthalten in Information systems frontiers Dordrecht [u.a.] : Springer Science + Business Media B.V, 1999 24(2021), 5 vom: 17. Aug., Seite 1689-1707 (DE-627)320530183 (DE-600)2015660-1 1572-9419 nnns volume:24 year:2021 number:5 day:17 month:08 pages:1689-1707 https://dx.doi.org/10.1007/s10796-021-10180-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 24 2021 5 17 08 1689-1707 |
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10.1007/s10796-021-10180-2 doi (DE-627)SPR04880620X (SPR)s10796-021-10180-2-e DE-627 ger DE-627 rakwb eng Zhu, Nafei verfasserin aut Ontology-Based Approach for the Measurement of Privacy Disclosure 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract 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. Privacy (dpeaa)DE-He213 Ontology (dpeaa)DE-He213 Privacy disclosure, privacy quantification (dpeaa)DE-He213 Chen, Baocun aut Wang, Siyu aut Teng, Da aut He, Jingsha aut Enthalten in Information systems frontiers Dordrecht [u.a.] : Springer Science + Business Media B.V, 1999 24(2021), 5 vom: 17. Aug., Seite 1689-1707 (DE-627)320530183 (DE-600)2015660-1 1572-9419 nnns volume:24 year:2021 number:5 day:17 month:08 pages:1689-1707 https://dx.doi.org/10.1007/s10796-021-10180-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 24 2021 5 17 08 1689-1707 |
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10.1007/s10796-021-10180-2 doi (DE-627)SPR04880620X (SPR)s10796-021-10180-2-e DE-627 ger DE-627 rakwb eng Zhu, Nafei verfasserin aut Ontology-Based Approach for the Measurement of Privacy Disclosure 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract 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. Privacy (dpeaa)DE-He213 Ontology (dpeaa)DE-He213 Privacy disclosure, privacy quantification (dpeaa)DE-He213 Chen, Baocun aut Wang, Siyu aut Teng, Da aut He, Jingsha aut Enthalten in Information systems frontiers Dordrecht [u.a.] : Springer Science + Business Media B.V, 1999 24(2021), 5 vom: 17. Aug., Seite 1689-1707 (DE-627)320530183 (DE-600)2015660-1 1572-9419 nnns volume:24 year:2021 number:5 day:17 month:08 pages:1689-1707 https://dx.doi.org/10.1007/s10796-021-10180-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 24 2021 5 17 08 1689-1707 |
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10.1007/s10796-021-10180-2 doi (DE-627)SPR04880620X (SPR)s10796-021-10180-2-e DE-627 ger DE-627 rakwb eng Zhu, Nafei verfasserin aut Ontology-Based Approach for the Measurement of Privacy Disclosure 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract 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. Privacy (dpeaa)DE-He213 Ontology (dpeaa)DE-He213 Privacy disclosure, privacy quantification (dpeaa)DE-He213 Chen, Baocun aut Wang, Siyu aut Teng, Da aut He, Jingsha aut Enthalten in Information systems frontiers Dordrecht [u.a.] : Springer Science + Business Media B.V, 1999 24(2021), 5 vom: 17. Aug., Seite 1689-1707 (DE-627)320530183 (DE-600)2015660-1 1572-9419 nnns volume:24 year:2021 number:5 day:17 month:08 pages:1689-1707 https://dx.doi.org/10.1007/s10796-021-10180-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 24 2021 5 17 08 1689-1707 |
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10.1007/s10796-021-10180-2 doi (DE-627)SPR04880620X (SPR)s10796-021-10180-2-e DE-627 ger DE-627 rakwb eng Zhu, Nafei verfasserin aut Ontology-Based Approach for the Measurement of Privacy Disclosure 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract 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. Privacy (dpeaa)DE-He213 Ontology (dpeaa)DE-He213 Privacy disclosure, privacy quantification (dpeaa)DE-He213 Chen, Baocun aut Wang, Siyu aut Teng, Da aut He, Jingsha aut Enthalten in Information systems frontiers Dordrecht [u.a.] : Springer Science + Business Media B.V, 1999 24(2021), 5 vom: 17. Aug., Seite 1689-1707 (DE-627)320530183 (DE-600)2015660-1 1572-9419 nnns volume:24 year:2021 number:5 day:17 month:08 pages:1689-1707 https://dx.doi.org/10.1007/s10796-021-10180-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 24 2021 5 17 08 1689-1707 |
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ontology-based approach for the measurement of privacy disclosure |
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Ontology-Based Approach for the Measurement of Privacy Disclosure |
| abstract |
Abstract 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. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstractGer |
Abstract 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. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract 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. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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| title_short |
Ontology-Based Approach for the Measurement of Privacy Disclosure |
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https://dx.doi.org/10.1007/s10796-021-10180-2 |
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Chen, Baocun Wang, Siyu Teng, Da He, Jingsha |
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Chen, Baocun Wang, Siyu Teng, Da He, Jingsha |
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| doi_str |
10.1007/s10796-021-10180-2 |
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2024-07-03T21:35:28.783Z |
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| score |
7.399703 |