PrivacyGrid Visualization Balaji Palanisamy Saurabh Taneja

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?

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.

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.

Spatial Cloaking

PrivacyGrid Architecture

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

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

Visualization Features 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

Visualizing Cloaking Box

Top-down and Bottom-up Cloaking

Tracking a Mobile User

Performance Metrics Success Rate Anonymization time Relative anonymity level Relative spatial resolution

Future Work 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

References [1] B. Bamba, L. Liu, P. Pesti and T. Wang. Supporting Anonymous Location Queries in Mobile Environments using PrivacyGrid. In WWW, [2] M. Mokbel, C. Chow, and W. Aref. The New Casper: Query Processing for Location Services without Compromising Privacy. In VLDB, [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 , Apr [4] B. Gedik and L. Liu. Location Privacy in Mobile Systems: A Personalized Anonymization Model, in ICDCS, [5]G. Ghinita, P. Kalnis, and S. Skiadopoulos. PRIVE: Anonymous Location-Based Queries in Distributed Mobile Systems. In WWW, [6] U.S. Geological Survey.

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