A decentralised approach to privacy preserving trajectory mining
Large volumes of mobility data is collected in various application domains. Enterprise applications are designed on the notion of centralised data control where the proprietary of the data rests with the enterprise and not with the user. This has consequences as evident by the occasional privacy bre...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Talat, Romana [verfasserIn] |
|---|
| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020transfer abstract |
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| Schlagwörter: |
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| Umfang: |
11 |
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| Übergeordnetes Werk: |
Enthalten in: Surgeon-patient matching based on pairwise comparisons information for elective surgery - Jiang, Yan-Ping ELSEVIER, 2020, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:102 ; year:2020 ; pages:382-392 ; extent:11 |
| Links: |
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| DOI / URN: |
10.1016/j.future.2019.07.068 |
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ELV048538930 |
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| 520 | |a Large volumes of mobility data is collected in various application domains. Enterprise applications are designed on the notion of centralised data control where the proprietary of the data rests with the enterprise and not with the user. This has consequences as evident by the occasional privacy breaches. Trajectory mining is an important data mining problem, however, trajectory data can disclose sensitive location information about users. In this work, we propose a decentralised blockchain-enabled privacy-preserving trajectory data mining framework where the proprietary of the data rests with the user and not with the enterprise. We formalise the privacy preservation in trajectory data mining settings, present a proposal for privacy preservation, and implement the solution as a proof-of-concept. A comprehensive experimental evaluation is conducted to assess the applicability of the system. The results show that the proposed system yields promising results for blockchain-enabled privacy preservation in user trajectory data. | ||
| 520 | |a Large volumes of mobility data is collected in various application domains. Enterprise applications are designed on the notion of centralised data control where the proprietary of the data rests with the enterprise and not with the user. This has consequences as evident by the occasional privacy breaches. Trajectory mining is an important data mining problem, however, trajectory data can disclose sensitive location information about users. In this work, we propose a decentralised blockchain-enabled privacy-preserving trajectory data mining framework where the proprietary of the data rests with the user and not with the enterprise. We formalise the privacy preservation in trajectory data mining settings, present a proposal for privacy preservation, and implement the solution as a proof-of-concept. A comprehensive experimental evaluation is conducted to assess the applicability of the system. The results show that the proposed system yields promising results for blockchain-enabled privacy preservation in user trajectory data. | ||
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| 650 | 7 | |a Decentralised trajectory mining |2 Elsevier | |
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10.1016/j.future.2019.07.068 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000901.pica (DE-627)ELV048538930 (ELSEVIER)S0167-739X(19)31390-1 DE-627 ger DE-627 rakwb eng 004 VZ 85.35 bkl 54.80 bkl Talat, Romana verfasserin aut A decentralised approach to privacy preserving trajectory mining 2020transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Large volumes of mobility data is collected in various application domains. Enterprise applications are designed on the notion of centralised data control where the proprietary of the data rests with the enterprise and not with the user. This has consequences as evident by the occasional privacy breaches. Trajectory mining is an important data mining problem, however, trajectory data can disclose sensitive location information about users. In this work, we propose a decentralised blockchain-enabled privacy-preserving trajectory data mining framework where the proprietary of the data rests with the user and not with the enterprise. We formalise the privacy preservation in trajectory data mining settings, present a proposal for privacy preservation, and implement the solution as a proof-of-concept. A comprehensive experimental evaluation is conducted to assess the applicability of the system. The results show that the proposed system yields promising results for blockchain-enabled privacy preservation in user trajectory data. Large volumes of mobility data is collected in various application domains. Enterprise applications are designed on the notion of centralised data control where the proprietary of the data rests with the enterprise and not with the user. This has consequences as evident by the occasional privacy breaches. Trajectory mining is an important data mining problem, however, trajectory data can disclose sensitive location information about users. In this work, we propose a decentralised blockchain-enabled privacy-preserving trajectory data mining framework where the proprietary of the data rests with the user and not with the enterprise. We formalise the privacy preservation in trajectory data mining settings, present a proposal for privacy preservation, and implement the solution as a proof-of-concept. A comprehensive experimental evaluation is conducted to assess the applicability of the system. The results show that the proposed system yields promising results for blockchain-enabled privacy preservation in user trajectory data. Privacy preservation Elsevier Trajectory data Elsevier Blockchain technology Elsevier Decentralised trajectory mining Elsevier Obaidat, Mohammad S. oth Muzammal, Muhammad oth Sodhro, Ali Hassan oth Luo, Zongwei oth Pirbhulal, Sandeep oth Enthalten in Elsevier Science Jiang, Yan-Ping ELSEVIER Surgeon-patient matching based on pairwise comparisons information for elective surgery 2020 Amsterdam [u.a.] (DE-627)ELV004280385 volume:102 year:2020 pages:382-392 extent:11 https://doi.org/10.1016/j.future.2019.07.068 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 85.35 Fertigung VZ 54.80 Angewandte Informatik VZ AR 102 2020 382-392 11 |
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10.1016/j.future.2019.07.068 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000901.pica (DE-627)ELV048538930 (ELSEVIER)S0167-739X(19)31390-1 DE-627 ger DE-627 rakwb eng 004 VZ 85.35 bkl 54.80 bkl Talat, Romana verfasserin aut A decentralised approach to privacy preserving trajectory mining 2020transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Large volumes of mobility data is collected in various application domains. Enterprise applications are designed on the notion of centralised data control where the proprietary of the data rests with the enterprise and not with the user. This has consequences as evident by the occasional privacy breaches. Trajectory mining is an important data mining problem, however, trajectory data can disclose sensitive location information about users. In this work, we propose a decentralised blockchain-enabled privacy-preserving trajectory data mining framework where the proprietary of the data rests with the user and not with the enterprise. We formalise the privacy preservation in trajectory data mining settings, present a proposal for privacy preservation, and implement the solution as a proof-of-concept. A comprehensive experimental evaluation is conducted to assess the applicability of the system. The results show that the proposed system yields promising results for blockchain-enabled privacy preservation in user trajectory data. Large volumes of mobility data is collected in various application domains. Enterprise applications are designed on the notion of centralised data control where the proprietary of the data rests with the enterprise and not with the user. This has consequences as evident by the occasional privacy breaches. Trajectory mining is an important data mining problem, however, trajectory data can disclose sensitive location information about users. In this work, we propose a decentralised blockchain-enabled privacy-preserving trajectory data mining framework where the proprietary of the data rests with the user and not with the enterprise. We formalise the privacy preservation in trajectory data mining settings, present a proposal for privacy preservation, and implement the solution as a proof-of-concept. A comprehensive experimental evaluation is conducted to assess the applicability of the system. The results show that the proposed system yields promising results for blockchain-enabled privacy preservation in user trajectory data. Privacy preservation Elsevier Trajectory data Elsevier Blockchain technology Elsevier Decentralised trajectory mining Elsevier Obaidat, Mohammad S. oth Muzammal, Muhammad oth Sodhro, Ali Hassan oth Luo, Zongwei oth Pirbhulal, Sandeep oth Enthalten in Elsevier Science Jiang, Yan-Ping ELSEVIER Surgeon-patient matching based on pairwise comparisons information for elective surgery 2020 Amsterdam [u.a.] (DE-627)ELV004280385 volume:102 year:2020 pages:382-392 extent:11 https://doi.org/10.1016/j.future.2019.07.068 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 85.35 Fertigung VZ 54.80 Angewandte Informatik VZ AR 102 2020 382-392 11 |
| allfields_unstemmed |
10.1016/j.future.2019.07.068 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000901.pica (DE-627)ELV048538930 (ELSEVIER)S0167-739X(19)31390-1 DE-627 ger DE-627 rakwb eng 004 VZ 85.35 bkl 54.80 bkl Talat, Romana verfasserin aut A decentralised approach to privacy preserving trajectory mining 2020transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Large volumes of mobility data is collected in various application domains. Enterprise applications are designed on the notion of centralised data control where the proprietary of the data rests with the enterprise and not with the user. This has consequences as evident by the occasional privacy breaches. Trajectory mining is an important data mining problem, however, trajectory data can disclose sensitive location information about users. In this work, we propose a decentralised blockchain-enabled privacy-preserving trajectory data mining framework where the proprietary of the data rests with the user and not with the enterprise. We formalise the privacy preservation in trajectory data mining settings, present a proposal for privacy preservation, and implement the solution as a proof-of-concept. A comprehensive experimental evaluation is conducted to assess the applicability of the system. The results show that the proposed system yields promising results for blockchain-enabled privacy preservation in user trajectory data. Large volumes of mobility data is collected in various application domains. Enterprise applications are designed on the notion of centralised data control where the proprietary of the data rests with the enterprise and not with the user. This has consequences as evident by the occasional privacy breaches. Trajectory mining is an important data mining problem, however, trajectory data can disclose sensitive location information about users. In this work, we propose a decentralised blockchain-enabled privacy-preserving trajectory data mining framework where the proprietary of the data rests with the user and not with the enterprise. We formalise the privacy preservation in trajectory data mining settings, present a proposal for privacy preservation, and implement the solution as a proof-of-concept. A comprehensive experimental evaluation is conducted to assess the applicability of the system. The results show that the proposed system yields promising results for blockchain-enabled privacy preservation in user trajectory data. Privacy preservation Elsevier Trajectory data Elsevier Blockchain technology Elsevier Decentralised trajectory mining Elsevier Obaidat, Mohammad S. oth Muzammal, Muhammad oth Sodhro, Ali Hassan oth Luo, Zongwei oth Pirbhulal, Sandeep oth Enthalten in Elsevier Science Jiang, Yan-Ping ELSEVIER Surgeon-patient matching based on pairwise comparisons information for elective surgery 2020 Amsterdam [u.a.] (DE-627)ELV004280385 volume:102 year:2020 pages:382-392 extent:11 https://doi.org/10.1016/j.future.2019.07.068 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 85.35 Fertigung VZ 54.80 Angewandte Informatik VZ AR 102 2020 382-392 11 |
| allfieldsGer |
10.1016/j.future.2019.07.068 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000901.pica (DE-627)ELV048538930 (ELSEVIER)S0167-739X(19)31390-1 DE-627 ger DE-627 rakwb eng 004 VZ 85.35 bkl 54.80 bkl Talat, Romana verfasserin aut A decentralised approach to privacy preserving trajectory mining 2020transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Large volumes of mobility data is collected in various application domains. Enterprise applications are designed on the notion of centralised data control where the proprietary of the data rests with the enterprise and not with the user. This has consequences as evident by the occasional privacy breaches. Trajectory mining is an important data mining problem, however, trajectory data can disclose sensitive location information about users. In this work, we propose a decentralised blockchain-enabled privacy-preserving trajectory data mining framework where the proprietary of the data rests with the user and not with the enterprise. We formalise the privacy preservation in trajectory data mining settings, present a proposal for privacy preservation, and implement the solution as a proof-of-concept. A comprehensive experimental evaluation is conducted to assess the applicability of the system. The results show that the proposed system yields promising results for blockchain-enabled privacy preservation in user trajectory data. Large volumes of mobility data is collected in various application domains. Enterprise applications are designed on the notion of centralised data control where the proprietary of the data rests with the enterprise and not with the user. This has consequences as evident by the occasional privacy breaches. Trajectory mining is an important data mining problem, however, trajectory data can disclose sensitive location information about users. In this work, we propose a decentralised blockchain-enabled privacy-preserving trajectory data mining framework where the proprietary of the data rests with the user and not with the enterprise. We formalise the privacy preservation in trajectory data mining settings, present a proposal for privacy preservation, and implement the solution as a proof-of-concept. A comprehensive experimental evaluation is conducted to assess the applicability of the system. The results show that the proposed system yields promising results for blockchain-enabled privacy preservation in user trajectory data. Privacy preservation Elsevier Trajectory data Elsevier Blockchain technology Elsevier Decentralised trajectory mining Elsevier Obaidat, Mohammad S. oth Muzammal, Muhammad oth Sodhro, Ali Hassan oth Luo, Zongwei oth Pirbhulal, Sandeep oth Enthalten in Elsevier Science Jiang, Yan-Ping ELSEVIER Surgeon-patient matching based on pairwise comparisons information for elective surgery 2020 Amsterdam [u.a.] (DE-627)ELV004280385 volume:102 year:2020 pages:382-392 extent:11 https://doi.org/10.1016/j.future.2019.07.068 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 85.35 Fertigung VZ 54.80 Angewandte Informatik VZ AR 102 2020 382-392 11 |
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10.1016/j.future.2019.07.068 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000901.pica (DE-627)ELV048538930 (ELSEVIER)S0167-739X(19)31390-1 DE-627 ger DE-627 rakwb eng 004 VZ 85.35 bkl 54.80 bkl Talat, Romana verfasserin aut A decentralised approach to privacy preserving trajectory mining 2020transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Large volumes of mobility data is collected in various application domains. Enterprise applications are designed on the notion of centralised data control where the proprietary of the data rests with the enterprise and not with the user. This has consequences as evident by the occasional privacy breaches. Trajectory mining is an important data mining problem, however, trajectory data can disclose sensitive location information about users. In this work, we propose a decentralised blockchain-enabled privacy-preserving trajectory data mining framework where the proprietary of the data rests with the user and not with the enterprise. We formalise the privacy preservation in trajectory data mining settings, present a proposal for privacy preservation, and implement the solution as a proof-of-concept. A comprehensive experimental evaluation is conducted to assess the applicability of the system. The results show that the proposed system yields promising results for blockchain-enabled privacy preservation in user trajectory data. Large volumes of mobility data is collected in various application domains. Enterprise applications are designed on the notion of centralised data control where the proprietary of the data rests with the enterprise and not with the user. This has consequences as evident by the occasional privacy breaches. Trajectory mining is an important data mining problem, however, trajectory data can disclose sensitive location information about users. In this work, we propose a decentralised blockchain-enabled privacy-preserving trajectory data mining framework where the proprietary of the data rests with the user and not with the enterprise. We formalise the privacy preservation in trajectory data mining settings, present a proposal for privacy preservation, and implement the solution as a proof-of-concept. A comprehensive experimental evaluation is conducted to assess the applicability of the system. The results show that the proposed system yields promising results for blockchain-enabled privacy preservation in user trajectory data. Privacy preservation Elsevier Trajectory data Elsevier Blockchain technology Elsevier Decentralised trajectory mining Elsevier Obaidat, Mohammad S. oth Muzammal, Muhammad oth Sodhro, Ali Hassan oth Luo, Zongwei oth Pirbhulal, Sandeep oth Enthalten in Elsevier Science Jiang, Yan-Ping ELSEVIER Surgeon-patient matching based on pairwise comparisons information for elective surgery 2020 Amsterdam [u.a.] (DE-627)ELV004280385 volume:102 year:2020 pages:382-392 extent:11 https://doi.org/10.1016/j.future.2019.07.068 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 85.35 Fertigung VZ 54.80 Angewandte Informatik VZ AR 102 2020 382-392 11 |
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Enthalten in Surgeon-patient matching based on pairwise comparisons information for elective surgery Amsterdam [u.a.] volume:102 year:2020 pages:382-392 extent:11 |
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Surgeon-patient matching based on pairwise comparisons information for elective surgery |
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004 VZ 85.35 bkl 54.80 bkl A decentralised approach to privacy preserving trajectory mining Privacy preservation Elsevier Trajectory data Elsevier Blockchain technology Elsevier Decentralised trajectory mining Elsevier |
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ddc 004 bkl 85.35 bkl 54.80 Elsevier Privacy preservation Elsevier Trajectory data Elsevier Blockchain technology Elsevier Decentralised trajectory mining |
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ddc 004 bkl 85.35 bkl 54.80 Elsevier Privacy preservation Elsevier Trajectory data Elsevier Blockchain technology Elsevier Decentralised trajectory mining |
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ddc 004 bkl 85.35 bkl 54.80 Elsevier Privacy preservation Elsevier Trajectory data Elsevier Blockchain technology Elsevier Decentralised trajectory mining |
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Surgeon-patient matching based on pairwise comparisons information for elective surgery |
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A decentralised approach to privacy preserving trajectory mining |
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A decentralised approach to privacy preserving trajectory mining |
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Talat, Romana |
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Surgeon-patient matching based on pairwise comparisons information for elective surgery |
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10.1016/j.future.2019.07.068 |
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a decentralised approach to privacy preserving trajectory mining |
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A decentralised approach to privacy preserving trajectory mining |
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Large volumes of mobility data is collected in various application domains. Enterprise applications are designed on the notion of centralised data control where the proprietary of the data rests with the enterprise and not with the user. This has consequences as evident by the occasional privacy breaches. Trajectory mining is an important data mining problem, however, trajectory data can disclose sensitive location information about users. In this work, we propose a decentralised blockchain-enabled privacy-preserving trajectory data mining framework where the proprietary of the data rests with the user and not with the enterprise. We formalise the privacy preservation in trajectory data mining settings, present a proposal for privacy preservation, and implement the solution as a proof-of-concept. A comprehensive experimental evaluation is conducted to assess the applicability of the system. The results show that the proposed system yields promising results for blockchain-enabled privacy preservation in user trajectory data. |
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Large volumes of mobility data is collected in various application domains. Enterprise applications are designed on the notion of centralised data control where the proprietary of the data rests with the enterprise and not with the user. This has consequences as evident by the occasional privacy breaches. Trajectory mining is an important data mining problem, however, trajectory data can disclose sensitive location information about users. In this work, we propose a decentralised blockchain-enabled privacy-preserving trajectory data mining framework where the proprietary of the data rests with the user and not with the enterprise. We formalise the privacy preservation in trajectory data mining settings, present a proposal for privacy preservation, and implement the solution as a proof-of-concept. A comprehensive experimental evaluation is conducted to assess the applicability of the system. The results show that the proposed system yields promising results for blockchain-enabled privacy preservation in user trajectory data. |
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Large volumes of mobility data is collected in various application domains. Enterprise applications are designed on the notion of centralised data control where the proprietary of the data rests with the enterprise and not with the user. This has consequences as evident by the occasional privacy breaches. Trajectory mining is an important data mining problem, however, trajectory data can disclose sensitive location information about users. In this work, we propose a decentralised blockchain-enabled privacy-preserving trajectory data mining framework where the proprietary of the data rests with the user and not with the enterprise. We formalise the privacy preservation in trajectory data mining settings, present a proposal for privacy preservation, and implement the solution as a proof-of-concept. A comprehensive experimental evaluation is conducted to assess the applicability of the system. The results show that the proposed system yields promising results for blockchain-enabled privacy preservation in user trajectory data. |
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A decentralised approach to privacy preserving trajectory mining |
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Obaidat, Mohammad S. Muzammal, Muhammad Sodhro, Ali Hassan Luo, Zongwei Pirbhulal, Sandeep |
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