Privacy-Preserving Aggregate Queries for Optimal Location Selection

Today, vast amounts of location data are collected by various service providers. These location data owners have a good idea of where their users are most of the time. Other businesses also want to use this information for location analytics, such as finding the optimal location for a new branch. Ho...

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Vydáno v:	IEEE transactions on dependable and secure computing Ročník 16; číslo 2; s. 329 - 343
Hlavní autoři:	Yilmaz, Emre, Ferhatosmanoglu, Hakan, Ayday, Erman, Aksoy, Remzi Can
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Washington IEEE 01.03.2019 IEEE Computer Society
Témata:	Aggregates algorithm/protocol design and analysis Analytics and protection Business Computer privacy Customers Cybersecurity data encryption Data privacy Encryption integrity Privacy Protocols Queries query processing security Servers
ISSN:	1545-5971, 1941-0018
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Abstract	Today, vast amounts of location data are collected by various service providers. These location data owners have a good idea of where their users are most of the time. Other businesses also want to use this information for location analytics, such as finding the optimal location for a new branch. However, location data owners cannot share their data with other businesses, mainly due to privacy and legal concerns. In this paper, we propose privacy-preserving solutions in which location-based queries can be answered by data owners without sharing their data with other businesses and without accessing sensitive information such as the customer list of the businesses that send the query. We utilize a partially homomorphic cryptosystem as the building block of the proposed protocols. We prove the security of the protocols in semi-honest threat model. We also explain how to achieve differential privacy in the proposed protocols and discuss its impact on utility. We evaluate the performance of the protocols with real and synthetic datasets and show that the proposed solutions are highly practical. The proposed solutions will facilitate an effective sharing of sensitive data between entities and joint analytics in a wide range of applications without violating their customers' privacy.
AbstractList	Today, vast amounts of location data are collected by various service providers. These location data owners have a good idea of where their users are most of the time. Other businesses also want to use this information for location analytics, such as finding the optimal location for a new branch. However, location data owners cannot share their data with other businesses, mainly due to privacy and legal concerns. In this paper, we propose privacy-preserving solutions in which location-based queries can be answered by data owners without sharing their data with other businesses and without accessing sensitive information such as the customer list of the businesses that send the query. We utilize a partially homomorphic cryptosystem as the building block of the proposed protocols. We prove the security of the protocols in semi-honest threat model. We also explain how to achieve differential privacy in the proposed protocols and discuss its impact on utility. We evaluate the performance of the protocols with real and synthetic datasets and show that the proposed solutions are highly practical. The proposed solutions will facilitate an effective sharing of sensitive data between entities and joint analytics in a wide range of applications without violating their customers’ privacy.
Author	Yilmaz, Emre Ayday, Erman Aksoy, Remzi Can Ferhatosmanoglu, Hakan
Author_xml	– sequence: 1 givenname: Emre orcidid: 0000-0003-0834-8805 surname: Yilmaz fullname: Yilmaz, Emre email: emre.yilmaz@cs.bilkent.edu.tr organization: Computer Engineering Department, Bilkent University, Ankara, Turkey – sequence: 2 givenname: Hakan surname: Ferhatosmanoglu fullname: Ferhatosmanoglu, Hakan email: hakan.f@warwick.ac.uk organization: Department of Computer Science, University of Warwick, Coventry, UK – sequence: 3 givenname: Erman orcidid: 0000-0003-3383-1081 surname: Ayday fullname: Ayday, Erman email: erman@cs.bilkent.edu.tr organization: Computer Engineering Department, Bilkent University, Ankara, Turkey – sequence: 4 givenname: Remzi Can surname: Aksoy fullname: Aksoy, Remzi Can email: remzican@umich.edu organization: Bilkent University, Ankara, Turkey
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SubjectTerms	Aggregates algorithm/protocol design and analysis Analytics and protection Business Computer privacy Customers Cybersecurity data encryption Data privacy Encryption integrity Privacy Protocols Queries query processing security Servers
Title	Privacy-Preserving Aggregate Queries for Optimal Location Selection
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