Privacy-preserving collaborative analytics of location data
Author(s)
Advisor
Date
2017-09Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
149
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Abstract
Deriving meaningful insights from location data helps businesses make better decisions.
While businesses must know the locations of their customers to perform
location analytics, most businesses do not have this valuable data. Location data
is typically collected by other services such as mobile telecommunication operators
and location-based service providers. We develop scalable privacy-preserving
solutions for collaborative analytics of location data. We propose two classes of
approaches for location analytics when businesses do not have the location data
of the customers. We illustrate both of our approaches in the context of optimal
location selection for the new branches of businesses. The rst type of approach is
retrieving the aggregate information about the customer locations from location
data owners via privacy-preserving queries. We de ne aggregate queries that can
be used in optimal location selection and we propose secure two-party protocols
for processing these queries. The proposed protocols utilize partially homomorphic
encryption as a building block and satisfy differential privacy. Our second
approach is to generate synthetic location data in order to perform analytics
without violating privacy of individuals. We propose a neighborhood-based data
generation method which can be used by businesses for predicting the optimal
location when they have partial information about customer locations. We also
propose grid-based and clustering-based data generation methods which can be
used by location data owners for publishing privacy-preserving synthetic location
data. Proposed approaches facilitate running optimal location queries by
businesses without knowing their customers' locations.
Keywords
Data PrivacyLocation Analytics
Optimal Location Queries
Differential Privacy
Homomorphic Encryption
Data Generation
Uncertainty