A Dummy-based Anonymization Method Based on User Trajectory with Pauses Ryo Kato, Mayu Iwata, Takahiro Hara, Akiyoshi Suzuki, Shojiro Nishio Osaka University Yuki Arase, Xing Xie Microsoft Research Asia ACM SIGSPATIAL GIS 2012

User 1 Overview Location privacy in LBS Extending k-Anonymous algorithm Does not need a trusted third-party server K-Anonymous server User 1 User 2 User 3 User 4 LBS provider k requests k responses Actual location + dummy locations

Related Work [12] – Moving in a neighborhood – Moving in a limited neighborhood [14] – Circle-based dummy – Uniform grid-based dummy [18] – Location Traceable Tree(LT-tree)

Dummy-based Approach

Restrictions in Real World Environment Consistency of movements – Consider actual road map information in order to generate reasonable dummy trajectories Traceability Anonymous area

Proposed Approach - Assumptions User continuously sends location to LBS provider Moving with some distribution of speed Stopping at several locations for a certain time Movement plans are known in advance

Proposed Approach Three Steps 1.Determine base pause position and base pause start time 2.Determine sets of shared pause positions and shared pause start times 3.Determine dummy’s movements

Determining Base Pause Position and Base Pause Start Time T0123…8 0s s s s00301 total58615 Base pause grid: 3 Base pause start time: 20s

Determining Sets of Shared Pause Positions and Shared Paused Start Times Reachable Base pause position & Base pause start time Reachable

Determining Dummy’s Movements T=3 T=12 T=14 T=28 Reachable Mid-pause position Mid-pause position Shared pause position Shared pause position Shared pause position Base pause position Mid-pause position T=9 T=19 T=22

Evaluation Setup Network simulator MobiREAL

Evaluation Metrics

Methods Comparison Previous method [17] – Similar method without pauses Proposed method Proposed method (AAAR-80) – Size of anonymous area varies greatly, low AAAR- Count found in some situations – Dynamically adjust anonymous area size to achieve 80% AAAR-Count

Result – AAAR-Count

Result – AAAR-Ratio

Result – AAAR-80

Result – MTC

Conclusion & Future Work The proposed approach generated dummies that moved naturally Real world restrictions taken into consideration Reactive dummies, does not need to know user’s movement plan in advance Real world experiment with real humans

Comments Did not mention communication and computation cost Prefer distribution/CDF plot over AAAR- Count/AAAR-Size percentage plot No additional third-party server is required Location accuracy is preserved