Privacy of Spatial Trajectories

Chow, Chee W.; Mokbel, Mohemad F.

doi:10.1007/978-1-4614-1629-6_4

Cited by 27 publications

(22 citation statements)

References 24 publications

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“…Various methods of user privacy preservation of spatial trajectory computation have been proposed [11], but most of them do not meet our need, consistent user identities. There are two methods, spatial cloaking and dummy locations/trajectories, has the feature of consistent user identities and are introduced in this subsection.…”

Section: B Privacy Preservation Of Spatial Trajectoriesmentioning

confidence: 99%

A novel matrix representation for privacy-preserving spatial trajectory prediction

Yang

Kaabouch

et al. 2013

IEEE International Conference on Electro-Information Technology , EIT 2013

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Location-based services (LBS), one of mobile applications, have attracted a great attention recently. This research proposes a location-based service, which predicts a spatial trajectory based on the current and previous trajectories by using a novel matrix representation. Spatial trajectory prediction can be used in a variety of purposes such as travel recommendations and traffic control and planning, but at the same time, just like most location-based services, the user privacy concern is a major issue. Without rigorous privacy protection, users would be reluctant to use the service. The proposed method is simple but effective and user privacy is rigorously preserved at the same time because the trajectory prediction is performed at the user-side. Additionally, this research is not only useful but also pedagogical because it involves a variety of topics like (i) mobile computing, (ii) mobile security, and (iii) human behavior recognition.

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Section: B Privacy Preservation Of Spatial Trajectoriesmentioning

confidence: 99%

A novel matrix representation for privacy-preserving spatial trajectory prediction

Yang

Kaabouch

et al. 2013

IEEE International Conference on Electro-Information Technology , EIT 2013

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“…Therefore, tracking data are stored without any reference to a user's identification. There are various anonymisers (Chow and Mokbel 2011). This paper uses the K-anonymity program , which is trusted third party software often used on tracking data (Chow and Mokbel 2011).…”

Section: Validation Using Crowd-sourced Trajectory Miningmentioning

confidence: 99%

“…There are various anonymisers (Chow and Mokbel 2011). This paper uses the K-anonymity program , which is trusted third party software often used on tracking data (Chow and Mokbel 2011). The data are stored in centralized systems or on decentralized peer devices (Ghinita, Kalnis, and Skiadopoulos 2007).…”

Section: Validation Using Crowd-sourced Trajectory Miningmentioning

confidence: 99%

Quality assessment of OpenStreetMap data using trajectory mining

Basiri

Jackson

Amirian

et al. 2016

Geo-spatial Information Science

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OpenStreetMap (OSM) data are widely used but their reliability is still variable. Many contributors to OSM have not been trained in geography or surveying and consequently their contributions, including geometry and attribute data inserts, deletions, and updates, can be inaccurate, incomplete, inconsistent, or vague. There are some mechanisms and applications dedicated to discovering bugs and errors in OSM data. Such systems can remove errors through user-checks and applying predefined rules but they need an extra control process to check the real-world validity of suspected errors and bugs. This paper focuses on finding bugs and errors based on patterns and rules extracted from the tracking data of users. The underlying idea is that certain characteristics of user trajectories are directly linked to the type of feature. Using such rules, some sets of potential bugs and errors can be identified and stored for further investigations.

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“…Let the dimensionality of each location point be l, that is, x i , y j ∈ R l . Table maintained during DTW computation between X = (2, 3), (3,5), (4,5), (3,4), (5,5) and Y = (2, 4), (3,4), (4,3), (3,5) , where d(, ) represents the squared Euclidean distance…”

Section: A Trajectory Similarity Measuresmentioning

confidence: 99%

“…The outsourcing of trajectory data, however, is a double-edged sword. An obvious concern is that it can seriously offense personal privacy [4], as the involved spatiotemporal data may reveal, either explicitly or by inference, many kinds of individual sensitive information such as home address, health condition, religious preference and political attitude. Therefore, instead of outsourcing trajectory data to the cloud directly, they should be processed appropriately to preserve the privacy of individuals.…”

Section: Introductionmentioning

confidence: 99%

Efficient secure similarity computation on encrypted trajectory data

Luo

Zhengy

Liz

et al. 2015

2015 IEEE 31st International Conference on Data Engineering

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Outsourcing database to clouds is a scalable and cost-effective way for large scale data storage, management, and query processing. Trajectory data contain rich spatiotemporal relationships and reveal many forms of individual sensitive information (e.g., home address, health condition), which necessitate them to be encrypted before being outsourced for privacy concerns. However, efficient query processing over encrypted trajectory data is a very challenging task. Though some achievements have been reported very recently for simple queries (e.g., SQL queries, kNN queries) on encrypted data, there is rather limited progress on secure evaluation of trajectory queries because they are more complex and need special treatment. In this paper, we focus on secure trajectory similarity computation that is the cornerstone of secure trajectory query processing. More specifically, we propose an efficient solution to securely compute the similarity between two encrypted trajectories, which reveals nothing about the trajectories, but the final result. We theoretically prove that our solution is secure against the semihonest adversaries model as all the intermediate information in our protocols can be simulated in polynomial time. Finally we empirically study the efficiency of the proposed method, which demonstrates the feasibility of our solution.978-1-4799-7964-6/15/$31.00

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Privacy of Spatial Trajectories

Cited by 27 publications

References 24 publications

A novel matrix representation for privacy-preserving spatial trajectory prediction

A novel matrix representation for privacy-preserving spatial trajectory prediction

Quality assessment of OpenStreetMap data using trajectory mining

Efficient secure similarity computation on encrypted trajectory data

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