A differentially private location generalization approach to guarantee non-uniform privacy in moving objects databases

2021 ◽  
pp. 107084
Author(s):  
Fatemeh Deldar ◽  
Mahdi Abadi
Keyword(s):  
Moving Objects ◽  
Private Location ◽  
Moving Objects Databases

Anonymization of moving objects databases by clustering and perturbation

2010 ◽  
Vol 35 (8) ◽  
pp. 884-910 ◽  
Author(s):  
Osman Abul ◽  
Francesco Bonchi ◽  
Mirco Nanni
Keyword(s):  
Moving Objects ◽  
Moving Objects Databases

scholarly journals Applications of Moving Objects Databases

2011 ◽  
pp. 186-203 ◽  
Author(s):  
Ouri Wolfson ◽  
Eduardo Mena
Keyword(s):  
Traffic Control ◽  
Moving Objects ◽  
Weather Forecasting ◽  
Location Management ◽  
Resource Discovery ◽  
Location Based Services ◽  
Sensor Technology ◽  
Moving Objects Databases ◽  
New Applications ◽  
Target Readership

Miniaturization of computing devices and advances in wireless communication and sensor technology are some of the forces propagating computing from the stationary desktop to the mobile outdoors. Some important classes of new applications that will be enabled by this revolutionary development include location-based services, tourist services, mobile electronic commerce and digital battlefield. Some existing application classes that will benefit from the development include transportation and air traffic control, weather forecasting, emergency response, mobile resource management and mobile workforce. Location management, that is, the management of transient location information, is an enabling technology for all these applications. Location management is also a fundamental component of other technologies, such as fly-through visualization, context awareness, augmented reality, cellular communication and dynamic resource discovery. Moving Objects Databases (MODs) store and manage the location as well as other dynamic information about moving objects. In this chapter we will present the applications of MODs and their functionality. The target readership is researchers and engineers working in databases and mobile computing.

Managing uncertainty in moving objects databases

2004 ◽  
Vol 29 (3) ◽  
pp. 463-507 ◽  
Author(s):  
Goce Trajcevski ◽  
Ouri Wolfson ◽  
Klaus Hinrichs ◽  
Sam Chamberlain
Keyword(s):  
Moving Objects ◽  
Moving Objects Databases

Benchmarking and Data Generation in Moving Objects Databases

2005 ◽  
pp. 23-28 ◽  
Author(s):  
Theodoros Tzouramanis
Keyword(s):  
Mobile Communication ◽  
Moving Objects ◽  
Query Languages ◽  
Location Information ◽  
Navigation Systems ◽  
Data Generation ◽  
Access Methods ◽  
Future Projections ◽  
Storage And Retrieval ◽  
Moving Objects Databases

Moving objects databases (MODs) provide the framework for the efficient storage and retrieval of the changing position of continuously moving objects. This includes the current and past locations of moving objects and the support of spatial queries that refer to historical location information and future projections as well. Nowadays, new spatiotemporal applications that require tracking and recording the trajectories of moving objects online are emerging. Digital battlefields, traffic supervision, mobile communication, navigation systems, and geographic information systems (GIS) are among these applications. Towards this goal, during recent years many efforts have focused on MOD formalism, data models, query languages, visualization, and access methods (Guting et al., 2000; Saltenis & Jensen, 2002; Sistla, Wolfson, Chamberlain, & Dao, 1997). However, little work has appeared on benchmarking.

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