Abstract

Data owners with large volumes of data can outsource spatial databases by taking advantage of the cost-effective cloud computing model with attractive on-demand features such as scalability and high computing power. Data confidentiality in outsourced databases is a key requirement and therefore, untrusted third-party service providers in the cloud should not be able to view or manipulate the data. This paper proposes DISC (Dynamic Index for Spatial data on the Cloud), a secure retrieval scheme to answer range queries over encrypted databases at the Cloud Service Provider. The dynamic spatial index is also able to support dynamic updates on the outsourced data at the cloud server. To be able to support secure query processing and updates on the Cloud, spatial transformation is applied to the data and the spatial index is encrypted using Order-Preserving Encryption. With transformation and cryptography techniques, DISC achieves a balance between efficient query execution and data confidentiality in a cloud environment. Additionally, a more secure scheme, DISC ∗, is proposed to balance the trade-off between query results returned and security provided. The security analysis section studies the various attacks handled by DISC. The experimental study demonstrates that the proposed scheme achieves a lower communication cost in comparison to existing cloud retrieval schemes.

Highlights

  • With increase in spatial data, data owners require the services of untrusted remote servers that can store huge amount of data and allow fast access to outsourced data

  • Spatial range queries performed at the cloud server must be completed in real-time and only relevant results should be returned to the user

  • We focus on the curious intruder model [31] to analyze the attacks posed to DISC, which requires Hilbert transformation of the data points before encryption

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Summary

Introduction

With increase in spatial data, data owners require the services of untrusted remote servers that can store huge amount of data and allow fast access to outsourced data. Cloud computing allows a third-party service provider to manage the data and provide services directly to the enduser. Cloud computing provides attractive features such as scalability, cost-effectiveness and high-computing power. Popular examples of cloud-based services include Google Maps and Amazon EC2. Mobile devices and navigational systems have become common and this has created the need for location-based services (LBSs). Mobile users issue queries from devices with limited storage and computational resources. Spatial range queries performed at the cloud server must be completed in real-time and only relevant results should be returned to the user

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