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Needs for Anonymized Mobile Data Discussion Topic / Working Group Seminar 08471
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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
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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
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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
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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
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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 Location@time 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
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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
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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
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Other “next steps” Seminar 08471: Geographic Privacy-Aware Knowledge Discovery and Delivery
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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
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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
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Context for Privacy Discussion Topic / Working Group Seminar 08471
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