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PrivacyGrid Visualization

PrivacyGrid Visualization. Balaji Palanisamy Saurabh Taneja. Location Based Services: Examples. Location-based Social Networking: Google Latitude: Where are my friends currently? Location-based advertisements:

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PrivacyGrid Visualization

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  1. PrivacyGrid Visualization Balaji Palanisamy Saurabh Taneja

  2. Location Based Services: Examples • Location-based Social Networking: • Google Latitude: Where are my friends currently? • Location-based advertisements: • Where are the gas stations within five miles of my location? • Location-based traffic Monitoring and Emergency services: • Show me the estimated time of travel to my destination?

  3. Location Privacy The capability of a mobile node (or a trusted location server) to conceal the relation between location information from third parties while the user is on the move. Threats Location-based technologies can pinpoint your location at any time and place. They promise safety and convenience but threaten privacy and security.

  4. PrivacyGrid Visualization Motivation • mobile users need to be aware of location privacy threats and the various location privacy metrics such as k-anonymity and l-diversity. • an effort to help naïve users appreciate the location privacy metrics and the location perturbation process in a mobile environment • For every query issued the user may wish to know the exposed location.

  5. Spatial Cloaking

  6. PrivacyGrid Architecture

  7. Location Privacy Metrics Quantitative Metrics: k-anonymity:location information is indistinguishable from k other users location l-diversity:reduces the risk of associating users with locations Each mobile user has his own privacy-profile that includes: 1. k-anonymity and l -diversity requirements 2. Maximum tolerable spatial resolution, dx and dy 3. Maximum tolerable temporal resolution, dt

  8. Spatial Cloaking in PrivacyGrid: 1. Bottom up Cloaking(dynamically adds grid cells) 2.Top Down Cloaking(dynamically reduces grid cells) 3. Hybrid Approach

  9. Works with any Geographical map User specified Traffic-volume and Traffic speed for each class of road( Expressways, Major roads, Residential roads) User specified simulation time Query by Query Navigation Tracking a specific user Various Grid-cell sizes Zoom-In Anonymization Statistics Visualization Features

  10. Visualizing Cloaking Box

  11. Top-down and Bottom-up Cloaking

  12. Tracking a Mobile User

  13. Performance Metrics • Success Rate • Anonymization time • Relative anonymity level • Relative spatial resolution

  14. Incorporate maps from other sources like Google maps in the Visualization tool. Visualize mobility of the objects. Visualize stepwise Top-down and Bottom-up expansion procedure Future Work

  15. References [1] B. Bamba, L. Liu, P. Pesti and T. Wang. Supporting Anonymous Location Queries in Mobile Environments using PrivacyGrid. In WWW, 2008. [2] M. Mokbel, C. Chow, and W. Aref. The New Casper: Query Processing for Location Services without Compromising Privacy. In VLDB, 2006. [3] Mohamed F. Mokbel, Chi-Yin Chow and Walid G. Aref. "The New Casper: A Privacy-Aware Location-Based Database Server". In Proceedings of the International Conference of Data Engineering,IEEE ICDE 2007, Istanbul, Turkey, pp. 1499-1500, Apr. 2007. [4] B. Gedik and L. Liu. Location Privacy in Mobile Systems: A Personalized Anonymization Model, in ICDCS, 2005. [5]G. Ghinita, P. Kalnis, and S. Skiadopoulos. PRIVE: Anonymous Location-Based Queries in Distributed Mobile Systems. In WWW, 2007. [6] U.S. Geological Survey. http://www.usgs.gov.

  16. Thank You

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