Privacy for Location-based Services

✍ Scribed by Gabriel Ghinita

• Publisher: Morgan & Claypool Publishers
• Year: 2013
• Tongue: English
• Leaves: 87
• Series: Synthesis Lectures on Information Security, Privacy, and Trust
• Category: Library

✦ Synopsis

Sharing of location data enables numerous exciting applications, such as location-based queries, location-based social recommendations, monitoring of traffic and air pollution levels, etc. Disclosing exact user locations raises serious privacy concerns, as locations may give away sensitive information about individuals' health status, alternative lifestyles, political and religious affiliations, etc. Preserving location privacy is an essential requirement towards the successful deployment of location-based applications. These lecture notes provide an overview of the state-of-the-art in location privacy protection. A diverse body of solutions is reviewed, including methods that use location generalization, cryptographic techniques or differential privacy. The most prominent results are discussed, and promising directions for future work are identified. Table of Contents: Introduction / Privacy-Preserving Spatial Transformations / Cryptographic Approaches / Hybrid Approaches / Private Matching of Spatial Datasets / Trajectory Anonymization / Differentially Private Publication of Spatial Datasets / Conclusions

✦ Table of Contents

Preface
Acknowledgments
Introduction
Privacy-Preserving Spatial Transformations
Two-Tier Spatial Transformations
Three-Tier Spatial Transformations
Discussion
Cryptographic Approaches
A Primer on Computational PIR
Spatial Queries with PIR
Protocols for Approximate and Exact NN with PIR
Comparison with Geometric Transformations
Hybrid Approaches
Privacy Model
System Overview
Private Evaluation of Point-Rectangle Enclosure
Private Evaluation of Point-Convex-Polygon Enclosure
Private Matching of Spatial Datasets
Problem Formulation
Dataset Mapping
Join Processing
Enhancing Privacy by Use of Chebyshev Distance
Trajectory Anonymization
Publishing Independent Location Samples
Publishing Individual Trajectories
Differentially Private Publication of Spatial Datasets
A Primer on Differential Privacy
Publication of Static Datasets of Locations
Data-Independent Decompositions: Quad-trees
Data-Dependent Decompositions: kd-trees
Publication of Trajectory Datasets
Conclusions
Bibliography
Author's Biography