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  Taxonomy of Dummy Generation Techniques for Preserving Location Privacy  
  Authors : Priti Jagwani; Ranjan Kumar
  Cite as: ijcsn.org/IJCSN-2013/2-6/IJCSN-2013-2-6-155.pdf


With the advent of GIS, Internet and mobile telephony, Location based services are becoming the need of the day. Privacy issues arising while using location based services have the potential to become the serious apprehensions. Existing privacy techniques based on anonymity generally fails in to offer the required privacy. We are focusing on the dummy generation techniques available in literature to protect location privacy. In these techniques, the actual location of a user along with several false position data (dummies) sent to the service provider. Because the service provider cannot distinguish the true position data, the user’s location privacy is protected. All the available algorithms to generate dummies (for query, locations as well as for trajectories) are reviewed. We briefly discussed and compared all the dummy generation techniques and listed the details of each technique.


Published In : IJCSN Journal Volume 2, Issue 6

Date of Publication : 01 December 2013

Pages : 174 - 178

Figures : 04

Tables : 04

Publication Link : ijcsn.org/IJCSN-2013/2-6/IJCSN-2013-2-6-155.pdf




Priti Jagwani : is pursuing PhD in Computer Science from School of IT, IIT Delhi. She has received her M.Tech degree in Computers from IIT Delhi in 2011. She is currently working as an Assistant Professor in Dept. of Computer Science, RLA (E) College, New Delhi, India. Her research interest is location privacy.

Ranjan Kumar : has done his MCA from dept of Computer Science , Univ of Delhi. He is currently working as an assistant professor in Dept. of Computer Science, RLA (E) College, New Delhi, India. His research areas are sensor networks, location based services.








Location Privacy

Dummy Generation Techniques

Plausible Deniable search

query privacy

location privacy




Dummy generation techniques for privacy are well suited with both types of architectures (with or without trusted third party). We highlighted dummy generation techniques available for location data, query and trajectory data. A domain of concern is generation of judicious dummies which cannot be distinguished from the real data. Plausible deniable search can be one promising way to address this concern. Although application of PDS for position data and location queries is a future research direction. For generation of judicious dummy data, use of location semantics can also be explored.










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