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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

    • 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


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