An Adaptive Window Size Selection Method for Differentially Private Data Publishing over Infinite Trajectory Stream
Recently, various services based on user's location are emerging since the development of wireless Internet and sensor technology. VANET (vehicular ad hoc network), in which a large number of vehicles communicate using wireless communication, is also being highlighted as one of the services. VA...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2018/8297678 |
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| _version_ | 1849415380207927296 |
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| author | Geonhyoung Jo Kangsoo Jung Seog Park |
| author_facet | Geonhyoung Jo Kangsoo Jung Seog Park |
| author_sort | Geonhyoung Jo |
| collection | DOAJ |
| description | Recently, various services based on user's location are emerging since the development of wireless Internet and sensor technology. VANET (vehicular ad hoc network), in which a large number of vehicles communicate using wireless communication, is also being highlighted as one of the services. VANET collects and analyzes the traffic data periodically to provide the traffic information service. The problem is that traffic data contains user’s sensitive location information that can lead to privacy violations. Differential privacy techniques are being used as a de facto standard to prevent such privacy violation caused by data analysis. However, applying differential privacy to traffic data stream which has infinite size over time makes data useless because too much noise is inserted to protect privacy. In order to overcome this limitation, existing researches set a certain range of windows and apply differential privacy to windowed data. However, previous researches have set a fixed window size do not consider a traffic data’s property such as road structure and time-based traffic variation. It may lead to insufficient privacy protection and unnecessary data utility degradation. In this paper, we propose an adaptive window size selection method that consider the correlation between road networks and time-based traffic variation to solve a fixed window size problem. And we suggest an adjustable privacy budget allocation technique for corresponding to the adaptive window size selection. We show that the proposed method improves the data utility, while satisfying the equal level of differential privacy as compared with the existing method through experiments that is designed based on real-world road network. |
| format | Article |
| id | doaj-art-69342426b8db437ab24e4e7a74c5a569 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-69342426b8db437ab24e4e7a74c5a5692025-08-20T03:33:32ZengWileyJournal of Advanced Transportation0197-67292042-31952018-01-01201810.1155/2018/82976788297678An Adaptive Window Size Selection Method for Differentially Private Data Publishing over Infinite Trajectory StreamGeonhyoung Jo0Kangsoo Jung1Seog Park2Computer Engineering Department, Sogang University, Republic of KoreaComputer Engineering Department, Sogang University, Republic of KoreaComputer Engineering Department, Sogang University, Republic of KoreaRecently, various services based on user's location are emerging since the development of wireless Internet and sensor technology. VANET (vehicular ad hoc network), in which a large number of vehicles communicate using wireless communication, is also being highlighted as one of the services. VANET collects and analyzes the traffic data periodically to provide the traffic information service. The problem is that traffic data contains user’s sensitive location information that can lead to privacy violations. Differential privacy techniques are being used as a de facto standard to prevent such privacy violation caused by data analysis. However, applying differential privacy to traffic data stream which has infinite size over time makes data useless because too much noise is inserted to protect privacy. In order to overcome this limitation, existing researches set a certain range of windows and apply differential privacy to windowed data. However, previous researches have set a fixed window size do not consider a traffic data’s property such as road structure and time-based traffic variation. It may lead to insufficient privacy protection and unnecessary data utility degradation. In this paper, we propose an adaptive window size selection method that consider the correlation between road networks and time-based traffic variation to solve a fixed window size problem. And we suggest an adjustable privacy budget allocation technique for corresponding to the adaptive window size selection. We show that the proposed method improves the data utility, while satisfying the equal level of differential privacy as compared with the existing method through experiments that is designed based on real-world road network.http://dx.doi.org/10.1155/2018/8297678 |
| spellingShingle | Geonhyoung Jo Kangsoo Jung Seog Park An Adaptive Window Size Selection Method for Differentially Private Data Publishing over Infinite Trajectory Stream Journal of Advanced Transportation |
| title | An Adaptive Window Size Selection Method for Differentially Private Data Publishing over Infinite Trajectory Stream |
| title_full | An Adaptive Window Size Selection Method for Differentially Private Data Publishing over Infinite Trajectory Stream |
| title_fullStr | An Adaptive Window Size Selection Method for Differentially Private Data Publishing over Infinite Trajectory Stream |
| title_full_unstemmed | An Adaptive Window Size Selection Method for Differentially Private Data Publishing over Infinite Trajectory Stream |
| title_short | An Adaptive Window Size Selection Method for Differentially Private Data Publishing over Infinite Trajectory Stream |
| title_sort | adaptive window size selection method for differentially private data publishing over infinite trajectory stream |
| url | http://dx.doi.org/10.1155/2018/8297678 |
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