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Differentially Private and Skew-Aware Spatial Decompositions for Mobile Crowdsensing.

Jong Seon Kim1, Yon Dohn Chung2, Jong Wook Kim3

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This study introduces SAGA, a novel partitioning technique for mobile crowdsensing (MCS) that enhances location privacy and improves spatial histogram accuracy. SAGA addresses limitations in existing methods by considering dataset domain size and hotspots for better worker privacy and query results.

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Area of Science:

  • Computer Science
  • Data Privacy
  • Mobile Computing

Background:

  • Mobile Crowdsensing (MCS) leverages mobile devices for cost-effective, large-scale sensor data collection.
  • Worker location disclosure during task assignment in MCS poses significant privacy risks.
  • Existing methods for differentially private spatial histograms often neglect dataset domain size, impacting accuracy.

Purpose of the Study:

  • To propose a novel partitioning technique, SAGA (Skew-Aware Grid Partitioning), for differentially private spatial histograms in MCS.
  • To enhance worker location privacy while maintaining accurate estimations of worker distribution.
  • To improve upon existing methods by addressing the domain size and hotspot characteristics of datasets.

Main Methods:

  • Developed SAGA, a partitioning technique that identifies and utilizes data hotspots.
  • Implemented a uniform grid partitioning within identified hotspots to optimize spatial indexing.
  • Applied differential privacy to publish sanitized spatial histograms, obscuring individual worker locations.

Main Results:

  • SAGA demonstrates enhanced query accuracy compared to existing methods across four real-world datasets.
  • The proposed method effectively preserves worker location privacy through differentially private spatial histograms.
  • Adjusting domain size based on hotspots leads to more accurate estimations of worker counts in arbitrary areas.

Conclusions:

  • SAGA offers a significant improvement in accuracy for differentially private spatial histograms in MCS.
  • The technique effectively balances location privacy with the utility of spatial data.
  • SAGA provides a robust solution for privacy-preserving data collection in mobile crowdsensing applications.