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dc.contributor.advisorAyday, Erman
dc.contributor.authorYılmaz, Emre
dc.date.accessioned2017-09-28T09:36:24Z
dc.date.available2017-09-28T09:36:24Z
dc.date.copyright2017-09
dc.date.issued2017-09
dc.date.submitted2017-09-27
dc.identifier.urihttp://hdl.handle.net/11693/33767
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (Ph.D.): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2017.en_US
dc.descriptionIncludes bibliographical references (leaves 98-103).en_US
dc.description.abstractDeriving 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.en_US
dc.description.statementofresponsibilityby Emre Yılmaz.en_US
dc.format.extentxi, 103 leaves : charts ; 30 cmen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectData Privacyen_US
dc.subjectLocation Analyticsen_US
dc.subjectOptimal Location Queriesen_US
dc.subjectDifferential Privacyen_US
dc.subjectHomomorphic Encryptionen_US
dc.subjectData Generationen_US
dc.subjectUncertaintyen_US
dc.titlePrivacy-preserving collaborative analytics of location dataen_US
dc.title.alternativeKonum verisinin gizliliğinin korunarak ortaklaşa analizien_US
dc.typeThesisen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreePh. D.en_US
dc.identifier.itemidB156504
dc.embargo.release2019-09-27


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