A Hybrid Approach for Privacy Preservation in Location Based Queries

Wu, Zhengang; Yu, Liangwen; Zhu, Jiawei; Sun, Huiping; Guan, Zhi; Chen, Zhong

doi:10.1007/978-3-642-39527-7_31

Cited by 1 publication

(1 citation statement)

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“…In [4], each user has a cloaked region to hide her exact location, and the probability of being within a range depends on the intersection of the cloaked regions. A hybrid approach that integrates private set intersection and location cloaking is presented in [26]. For privacy-preserving NN queries, a privacy-aware query processing framework called Casper is presented in [19].…”

Section: Privacy-preserving Location-based Query Processingmentioning

confidence: 99%

Privacy-Preserving Aggregate Queries for Optimal Location Selection

Yılmaz

Ferhatosmanoğlu

Ayday

et al. 2019

IEEE Trans. Dependable and Secure Comput.

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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.Comment: IEEE Transactions on Dependable and Secure Computing, 201

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Section: Privacy-preserving Location-based Query Processingmentioning

confidence: 99%