Needs for Anonymized Mobile Data Discussion Topic / Working Group Seminar 08471

What do we need to learn?  Applications Importance Societal Supportable Privacy constraints  Knowledge What information must be present in the data?  Structure How should the data be represented to make learning easy? Seminar 08471: Geographic Privacy-Aware Knowledge Discovery and Delivery

The Killer App(s) for Anonymized Data  Context and Location Aware Services When can we have expectation of privacy (sensors)? · Expectation “in a crowd” vs. “in the Wald” Public Safety Emergency response, evacuation Public security / law enforcement Lookup/location advertising Business workflows – factory, logistics – real-time response Traffic / transportation Mixed-reality games Enhanced tourism / Edutainment  Location Microdata Public Safety Planning Investigation Health research Personal health-related data (e.g., exercise data, environmental sensors) Epidemiology, pathology Collaborative filtering / collaborative recommendation Geomarketing Business workflows – factory, logistics – real-time response Urban planning Seminar 08471: Geographic Privacy-Aware Knowledge Discovery and Delivery

Information Required  Frequent vs. outlier  Location vs. trajectory  Data quality Exact? Probabilistic? Generalization of truth? Trajectory Patterns (Dino) example of learning that involves approximation Seminar 08471: Geographic Privacy-Aware Knowledge Discovery and Delivery

Real-time traffic analysis and services (Infomobility): Information Required  Frequent vs. outlier Outlier events Frequent normality  Location vs. trajectory Generally want trajectory, planned destination Aggregate data largely sufficient Sometimes point data sufficient (e.g., accident)  Service: Need to know current location, destination Can this be provided anonymously?  Background information Road network Calendar / events  Data quality / Granularity Granularity: road segment Outlier events – exact Frequency – probably want relatively close to exact, particularly when near capacity Seminar 08471: Geographic Privacy-Aware Knowledge Discovery and Delivery

Research on anonymized (geo) Health Info.: Information Required  Geospatial information Sensor-based / atmospheric conditions Geography – relevant semantics Telemedicine – magnifies geospatial variables Ex: Continuous heart monitoring  Frequent vs. outlier Outlier population / Adverse Drug Events Sporadic events (e.g., heart conditions)  Location vs. trajectory referenced with conditions Conditions inferred from trajectory and georeferenced data Correlation between individuals based on colocation (not necessarily in time)  Data quality Exact? Probabilistic? Generalization of truth? (Don’t tell them what the real data is)  Define policy before technology hits the market Seminar 08471: Geographic Privacy-Aware Knowledge Discovery and Delivery

Privacy and Web 2.0  Change in sensitivity?  What does privacy mean when people volunteer/publish data? (Particularly mobile/georeferenced data)  Interplay of privacy and trust  Do people know what they are giving up? Inference Archival  Psychological privacy vs. quantifiable risk  Context for privacy How does integration of other data with location affect privacy? Anonymity in the presence of external information? Seminar 08471: Geographic Privacy-Aware Knowledge Discovery and Delivery

Seminar Proceedings Killer App  Traffic Data  Health Data Research Web 2.0 outline  Kinds of geospatial self- published data  Uses  Risks / (Mis)uses  What do we do about this? Education Regulation Policy Technology Risk Assessment Research Agenda Seminar 08471: Geographic Privacy-Aware Knowledge Discovery and Delivery

Other “next steps” Seminar 08471: Geographic Privacy-Aware Knowledge Discovery and Delivery

Seminar Proceedings  Killer Apps. for anonymized data Description Data needs Anonymity/privacy  Traffic Data  Health  Privacy in Web 2.0 What is self-published geospatial data? Uses/value? Privacy concerns: Risk Perceptions Recommendations Seminar 08471: Geographic Privacy-Aware Knowledge Discovery and Delivery

Data Representation Enable use of existing tools?  Identical to real data Reconstruct representative trajectories (Saygin, Nergiz, Atzori GIS’08)  Region bounds  Region distributions (PDF) Seminar 08471: Geographic Privacy-Aware Knowledge Discovery and Delivery

Context for Privacy Discussion Topic / Working Group Seminar 08471