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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Veröffentlicht in:IEEE transactions on dependable and secure computing Jg. 16; H. 2; S. 329 - 343
Hauptverfasser: Yilmaz, Emre, Ferhatosmanoglu, Hakan, Ayday, Erman, Aksoy, Remzi Can
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Washington IEEE 01.03.2019
IEEE Computer Society
Schlagworte:
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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Zusammenfassung: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.
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ISSN:1545-5971
1941-0018
DOI:10.1109/TDSC.2017.2693986