The Experts below are selected from a list of 62550 Experts worldwide ranked by ideXlab platform
Marco Gruteser - One of the best experts on this subject based on the ideXlab platform.
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Privacy Protection method for fine grained urban traffic modeling using mobile sensors
Transportation Research Part B-methodological, 2013Co-Authors: Zhanbo Sun, Bin Zan, Xuegang Ban, Marco GruteserAbstract:With the ubiquitous nature of mobile sensing technologies, Privacy issues are becoming increasingly important, and need to be carefully addressed. Data needs for transportation modeling and Privacy Protection should be deliberately balanced for different applications. This paper focuses on developing Privacy mechanisms that would simultaneously satisfy Privacy Protection and data needs for fine-grained urban traffic modeling applications using mobile sensors. To accomplish this, a virtual trip lines (VTLs) zone-based system and related filtering approaches are developed. Traffic-knowledge-based adversary models are proposed and tested to evaluate the effectiveness of such a Privacy Protection system by making Privacy attacks. The results show that in addition to ensuring an acceptable level of Privacy, the released datasets from the Privacy-enhancing system can also be applied to urban traffic modeling with satisfactory results. Albeit application-specific, such a “Privacy-by-Design” approach would hopefully shed some light on other transportation applications using mobile sensors.
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Privacy Protection method for fine grained urban traffic modeling using mobile sensors
Transportation Research Board 92nd Annual MeetingTransportation Research Board, 2013Co-Authors: Zhanbo Sun, Bin Zan, Xuegang Ban, Marco GruteserAbstract:Privacy in transportation is controversial and under-studied. With the ubiquitous applications of Intelligent Transportation System (ITS) technologies, Privacy issues in transportation are becoming increasingly important and need to be addressed carefully. As a well-known trade-off, data needs and Privacy Protection should be deliberately balanced for different applications. This paper focuses on developing Privacy mechanisms to simultaneously satisfy Privacy Protection and modeling needs for fine-grained urban traffic modeling using mobile sensors. To accomplish this, a virtual trip lines (VTL) zone-based system and related filtering approaches are developed. Traffic-knowledge-based adversary models are proposed to evaluate the effectiveness of such system by making Privacy attacks. The results show that besides ensuring an acceptable level of Privacy, the released datasets from such Privacy-enhancing system can also be applied to traffic applications with satisfactory performance. Albeit application specific, such “Privacy-by-Design” approach would hopefully shed some light on other applications.
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Mobile Sensors as Traffic Probes: Addressing Transportation Modeling and Privacy Protection in an Integrated Framework
Traffic and Transportation Studies 2010, 2010Co-Authors: Xuegang Jeff Ban, Marco GruteserAbstract:Mobile traffic sensor data, once widely available, will significantly enhance current transportation modeling applications such as arterial performance measurement. Receiving and processing mobile sensor data however may involve severe Privacy concerns if not properly designed. In contrast to the fact that the current research on transportation modeling and Privacy Protection is rather separated, the authors propose in this paper a framework on Privacy-aware transportation modeling (PATM) and application-aware Privacy Protection (AAPP). The proposed framework focuses on the interactions between transportation modeling and Privacy preserving, being aware of Privacy when developing transportation models as well as application needs when designing Privacy preserving mechanisms. Using two case studies, the authors show how PATM and AAPP may be applied in Privacy-preserving mobile data collection while satisfying the application needs at the same time. The paper is concluded by discussing how to design a unified approach to guarantee Privacy and data needs for various applications.
Zhanbo Sun - One of the best experts on this subject based on the ideXlab platform.
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Privacy Protection method for fine grained urban traffic modeling using mobile sensors
Transportation Research Part B-methodological, 2013Co-Authors: Zhanbo Sun, Bin Zan, Xuegang Ban, Marco GruteserAbstract:With the ubiquitous nature of mobile sensing technologies, Privacy issues are becoming increasingly important, and need to be carefully addressed. Data needs for transportation modeling and Privacy Protection should be deliberately balanced for different applications. This paper focuses on developing Privacy mechanisms that would simultaneously satisfy Privacy Protection and data needs for fine-grained urban traffic modeling applications using mobile sensors. To accomplish this, a virtual trip lines (VTLs) zone-based system and related filtering approaches are developed. Traffic-knowledge-based adversary models are proposed and tested to evaluate the effectiveness of such a Privacy Protection system by making Privacy attacks. The results show that in addition to ensuring an acceptable level of Privacy, the released datasets from the Privacy-enhancing system can also be applied to urban traffic modeling with satisfactory results. Albeit application-specific, such a “Privacy-by-Design” approach would hopefully shed some light on other transportation applications using mobile sensors.
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Privacy Protection method for fine grained urban traffic modeling using mobile sensors
Transportation Research Board 92nd Annual MeetingTransportation Research Board, 2013Co-Authors: Zhanbo Sun, Bin Zan, Xuegang Ban, Marco GruteserAbstract:Privacy in transportation is controversial and under-studied. With the ubiquitous applications of Intelligent Transportation System (ITS) technologies, Privacy issues in transportation are becoming increasingly important and need to be addressed carefully. As a well-known trade-off, data needs and Privacy Protection should be deliberately balanced for different applications. This paper focuses on developing Privacy mechanisms to simultaneously satisfy Privacy Protection and modeling needs for fine-grained urban traffic modeling using mobile sensors. To accomplish this, a virtual trip lines (VTL) zone-based system and related filtering approaches are developed. Traffic-knowledge-based adversary models are proposed to evaluate the effectiveness of such system by making Privacy attacks. The results show that besides ensuring an acceptable level of Privacy, the released datasets from such Privacy-enhancing system can also be applied to traffic applications with satisfactory performance. Albeit application specific, such “Privacy-by-Design” approach would hopefully shed some light on other applications.
Xuegang Ban - One of the best experts on this subject based on the ideXlab platform.
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Privacy Protection method for fine grained urban traffic modeling using mobile sensors
Transportation Research Part B-methodological, 2013Co-Authors: Zhanbo Sun, Bin Zan, Xuegang Ban, Marco GruteserAbstract:With the ubiquitous nature of mobile sensing technologies, Privacy issues are becoming increasingly important, and need to be carefully addressed. Data needs for transportation modeling and Privacy Protection should be deliberately balanced for different applications. This paper focuses on developing Privacy mechanisms that would simultaneously satisfy Privacy Protection and data needs for fine-grained urban traffic modeling applications using mobile sensors. To accomplish this, a virtual trip lines (VTLs) zone-based system and related filtering approaches are developed. Traffic-knowledge-based adversary models are proposed and tested to evaluate the effectiveness of such a Privacy Protection system by making Privacy attacks. The results show that in addition to ensuring an acceptable level of Privacy, the released datasets from the Privacy-enhancing system can also be applied to urban traffic modeling with satisfactory results. Albeit application-specific, such a “Privacy-by-Design” approach would hopefully shed some light on other transportation applications using mobile sensors.
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Privacy Protection method for fine grained urban traffic modeling using mobile sensors
Transportation Research Board 92nd Annual MeetingTransportation Research Board, 2013Co-Authors: Zhanbo Sun, Bin Zan, Xuegang Ban, Marco GruteserAbstract:Privacy in transportation is controversial and under-studied. With the ubiquitous applications of Intelligent Transportation System (ITS) technologies, Privacy issues in transportation are becoming increasingly important and need to be addressed carefully. As a well-known trade-off, data needs and Privacy Protection should be deliberately balanced for different applications. This paper focuses on developing Privacy mechanisms to simultaneously satisfy Privacy Protection and modeling needs for fine-grained urban traffic modeling using mobile sensors. To accomplish this, a virtual trip lines (VTL) zone-based system and related filtering approaches are developed. Traffic-knowledge-based adversary models are proposed to evaluate the effectiveness of such system by making Privacy attacks. The results show that besides ensuring an acceptable level of Privacy, the released datasets from such Privacy-enhancing system can also be applied to traffic applications with satisfactory performance. Albeit application specific, such “Privacy-by-Design” approach would hopefully shed some light on other applications.
Bin Zan - One of the best experts on this subject based on the ideXlab platform.
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Privacy Protection method for fine grained urban traffic modeling using mobile sensors
Transportation Research Part B-methodological, 2013Co-Authors: Zhanbo Sun, Bin Zan, Xuegang Ban, Marco GruteserAbstract:With the ubiquitous nature of mobile sensing technologies, Privacy issues are becoming increasingly important, and need to be carefully addressed. Data needs for transportation modeling and Privacy Protection should be deliberately balanced for different applications. This paper focuses on developing Privacy mechanisms that would simultaneously satisfy Privacy Protection and data needs for fine-grained urban traffic modeling applications using mobile sensors. To accomplish this, a virtual trip lines (VTLs) zone-based system and related filtering approaches are developed. Traffic-knowledge-based adversary models are proposed and tested to evaluate the effectiveness of such a Privacy Protection system by making Privacy attacks. The results show that in addition to ensuring an acceptable level of Privacy, the released datasets from the Privacy-enhancing system can also be applied to urban traffic modeling with satisfactory results. Albeit application-specific, such a “Privacy-by-Design” approach would hopefully shed some light on other transportation applications using mobile sensors.
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Privacy Protection method for fine grained urban traffic modeling using mobile sensors
Transportation Research Board 92nd Annual MeetingTransportation Research Board, 2013Co-Authors: Zhanbo Sun, Bin Zan, Xuegang Ban, Marco GruteserAbstract:Privacy in transportation is controversial and under-studied. With the ubiquitous applications of Intelligent Transportation System (ITS) technologies, Privacy issues in transportation are becoming increasingly important and need to be addressed carefully. As a well-known trade-off, data needs and Privacy Protection should be deliberately balanced for different applications. This paper focuses on developing Privacy mechanisms to simultaneously satisfy Privacy Protection and modeling needs for fine-grained urban traffic modeling using mobile sensors. To accomplish this, a virtual trip lines (VTL) zone-based system and related filtering approaches are developed. Traffic-knowledge-based adversary models are proposed to evaluate the effectiveness of such system by making Privacy attacks. The results show that besides ensuring an acceptable level of Privacy, the released datasets from such Privacy-enhancing system can also be applied to traffic applications with satisfactory performance. Albeit application specific, such “Privacy-by-Design” approach would hopefully shed some light on other applications.
Touradj Ebrahimi - One of the best experts on this subject based on the ideXlab platform.
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Scrambling for Privacy Protection in Video Surveillance Systems
2010Co-Authors: Frederic Dufaux, Touradj EbrahimiAbstract:Abstract—In this paper, we address the problem of Privacy Protection in video surveillance. We introduce two efficient approaches to conceal regions of interest (ROIs) based on transform-domain or codestream-domain scrambling. In the first technique, the sign of selected transform coefficients is pseudorandomly flipped during encoding. In the second method, some bits of the codestream are pseudorandomly inverted. We address more specifically the cases of MPEG-4 as it is today the prevailing standard in video surveillance equipment. Simulations show that both techniques successfully hide private data in ROIs while the scene remains comprehensible. Additionally, the amount of noise introduced by the scrambling process can be adjusted. Finally, the impact on coding efficiency performance is small, and the required computational complexity is negligible. Index Terms—Privacy, selective encryption, surveillance, video processing
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a framework for the validation of Privacy Protection solutions in video surveillance
International Conference on Multimedia and Expo, 2010Co-Authors: Frederic Dufaux, Touradj EbrahimiAbstract:The issue of Privacy Protection in video surveillance has drawn a lot of interest lately. However, thorough performance analysis and validation is still lacking, especially regarding the fulfillment of Privacy-related requirements. In this paper, we put forward a framework to assess the capacity of Privacy Protection solutions to hide distinguishing facial information and to conceal identity. We then conduct rigorous experiments to evaluate the performance of face recognition algorithms applied to images altered by Privacy Protection techniques. Results show the ineffectiveness of naive Privacy Protection techniques such as pixelization and blur. Conversely, they demonstrate the effectiveness of more sophisticated scrambling techniques to foil face recognition.
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Scrambling for Privacy Protection in Video Surveillance Systems
IEEE Transactions on Circuits and Systems for Video Technology, 2008Co-Authors: Frederic Dufaux, Touradj EbrahimiAbstract:In this paper, we address the problem of Privacy Protection in video surveillance. We introduce two efficient approaches to conceal regions of interest (ROIs) based on transform-domain or codestream-domain scrambling. In the first technique, the sign of selected transform coefficients is pseudorandomly flipped during encoding. In the second method, some bits of the codestream are pseudorandomly inverted. We address more specifically the cases of MPEG-4 as it is today the prevailing standard in video surveillance equipment. Simulations show that both techniques successfully hide private data in ROIs while the scene remains comprehensible. Additionally, the amount of noise introduced by the scrambling process can be adjusted. Finally, the impact on coding efficiency performance is small, and the required computational complexity is negligible.
