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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

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needs for anonymized mobile data

Needs for Anonymized Mobile Data

Discussion Topic / Working Group

Seminar 08471

what do we need to learn
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
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
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
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
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
    • [email protected] 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
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
Seminar Proceedings

Killer App

  • Traffic Data
  • Health Data Research

Web 2.0 outline

  • Kinds of geospatial self-published data
  • Uses
  • Risks / (Mis)uses

<above 1 axis, below 2nd axis>

  • 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
Other “next steps”

Seminar 08471: Geographic Privacy-Aware Knowledge Discovery and Delivery

seminar proceedings1
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
Data RepresentationEnable 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

Context for Privacy

Discussion Topic / Working Group

Seminar 08471

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