Preserving Privacy in Participatory Sensing Systems

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Preserving Privacy in Participatory Sensing
Systems

• Authors Kuan Lun Huang, Salil S. Kanhere (School
  of CS Engg., The University of New South Wales,
  Sydney, Australia),
• Wen Hu (Autonomous Systems Lab, CSIRO ICT Centre,
  Australia)
• Journal Computer Communications (Vol 33 Issue
  11, July 10)
• Publisher Butterworth-Heinemann Newton, MA, USA
  (Partly published at PerSeNs 09)
• Presented by Sara Gaffar

2
Contents

• Introduction
• A review of AnonySense
• Related Work
• System Model Motivating example
• Implementation Evaluation
• Important References

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Two Major Attributes

• This paper focuses on the spatial and temporal
  privacy of users, the two universal attributes
  expected to be included in user reports for all
  participatory sensing applications.

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Assumptions

• The adversary does not know true values of time
  and location of user reports. However, the
  adversary has means to find out the temporal and
  spatial properties of his victims.
• The adversary is able to observe submitted
  reports (eavesdropping).

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AnonySense Architecture
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Tessellation Generalization
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Perturbation Techniques

• Microaggregation and VMDAV
• Interpretation by Application Server by Euclidean
  Distance In the Euclidean plane, if p  (p1, p2)
  and q  (q1, q2) then the distance is given by
• d(p,q) v(p1-q1)² (p2-q2)²

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Problems with k-anonymity

• Tessellation Generalization
• Identity disclosure
• Attribute disclosure
• Background Knowledge Attack
• Homogeneity Attack
• The example of Bob
• L-diversity

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System Model
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Anonymization Server (AS)
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Petrolwatch

• An application which allows users to collect,
  contribute and share fuel pricing information
  using camera phones.
• Fuel prices are annotated with location
  coordinates of the service station and the time
  at which the capture takes place, and uploaded to
  the application server.
• Users can query the server to locate the cheapest
  petrol station in their vicinity.

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K-anonymous Privacy-Preserving Schemes

• Tessellation
• Tessellation with tile center reporting (TwTCR)
• Location anonymization with microaggregation -
  VMDAV
• Location anonymization with Hybrid
  microaggregation

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VMDAV Pseudo code
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Hybrid Microaggregation
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• VMDAV enables an application to make better
  decisions when user distributions across
  different areas are relatively consistent
• On the contrary, in areas with dense distribution
  of users, TwTCR performs better

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Gaussian Input Perturbation

• Why trust the AS?
• Perturbation Scheme Artificially distort a
  users location prior to updating the AS.

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L-Diversity

• Spatial AND temporal privacy
• Homogeneity and background knowledge attack

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Eg. 3-Anonymous Petrolwatch
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Example of 2-Diversity in terms of location
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Two Issues

• Semantic relationship between locations
• Timing accuracy

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LD-VMDAV
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1st Step
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2nd Step
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Evaluation
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Metrics

• Application accuracy Positive Identification
  Percentage (PIP)

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• Errors introduced by anonymization Information
  Loss (IL)

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Hybrid-VMDAV

• Improves percentage of positive identifications
  made by an application server by up to 100 and
  decreases amount of information loss by about 40

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LD-VMDAV Vs k-anonymity
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References

• Cory Cornelius , Apu Kapadia , David Kotz , Dan
  Peebles , Minho Shin , Nikos Triandopoulos,
  Anonysense privacy-aware people-centric sensing,
  Proceeding of the 6th international conference on
  Mobile systems, applications, and services, June
  17-20, 2008, Breckenridge, CO, USA 
• A. Solanas, A Martinez-Baellest. V-MDAV a
  multivariate microaggregation with variable group
  size, in 17th COMPSTAT Symposium of the IASC,
  Rome, 2006.