PRIVACY-PRESERVING AGGREGATE QUERIES FOROPTIMAL LOCATION SELECTION

 

ABSTRACT

Today, vast amounts of location data are collected by various service providers. The location data owners have a good ideaof where their customers are most of the time. Other businesses also want to use this information for location analytics, such as findingthe optimal location for a new branch. However, location data owners cannot directly share their data with other businesses, mainly dueto privacy and legal concerns. In this paper, we propose privacy-preserving solutions in which location-based queries can be executedand answered by location data owners without sharing their data with other businesses and without accessing the customer list of thebusinesses that send the query. We utilize a partially homomorphic cryptosystem as the building block of the proposed protocols. Weprove the security of the protocols in semi-honest threat model. We also explain how to achieve differential privacy in the proposedprotocols and discuss its impact on utility. We evaluate the performance of the protocols with real and synthetic datasets and show thatthe proposed solutions are highly practical. The proposed solutions will facilitate the sharing of sensitive data between entities in a widerange of applications without violating their customers’ privacy.

CONCLUSION:

We proposed novel protocols for privacy-preserving analysisof location data in a location-based service provider(referred as the server) by abusiness

 

 

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