• About
  • Policies
  • What is open access
  • Library
  • Contact
Advanced search
      View Item 
      •   BUIR Home
      • University Library
      • Bilkent Theses
      • Theses - Department of Computer Engineering
      • Dept. of Computer Engineering - Ph.D. / Sc.D.
      • View Item
      •   BUIR Home
      • University Library
      • Bilkent Theses
      • Theses - Department of Computer Engineering
      • Dept. of Computer Engineering - Ph.D. / Sc.D.
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.

      Privacy-preserving collaborative analytics of location data

      Thumbnail
      Embargo Lift Date: 2019-09-27
      View / Download
      10.2 Mb
      Author(s)
      Yılmaz, Emre
      Advisor
      Ayday, Erman
      Date
      2017-09
      Publisher
      Bilkent University
      Language
      English
      Type
      Thesis
      Item Usage Stats
      149
      views
      43
      downloads
      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 Privacy
      Location Analytics
      Optimal Location Queries
      Differential Privacy
      Homomorphic Encryption
      Data Generation
      Uncertainty
      Permalink
      http://hdl.handle.net/11693/33767
      Collections
      • Dept. of Computer Engineering - Ph.D. / Sc.D. 75
      Show full item record

      Browse

      All of BUIRCommunities & CollectionsTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartmentsThis CollectionTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartments

      My Account

      LoginRegister

      Statistics

      View Usage StatisticsView Google Analytics Statistics

      Bilkent University

      If you have trouble accessing this page and need to request an alternate format, contact the site administrator. Phone: (312) 290 1771
      © Bilkent University - Library IT

      Contact Us | Send Feedback | Off-Campus Access | Admin | Privacy