Abstract
The now ubiquitous use of Location based services (LBS), within the mobile computing domain, has enabled users to receive accurate points of interest (POI) to their geo-tagged queries. While location-based services provide rich content, they are not without risks; specifically, the use of LBS poses many serious challenges with respect to privacy protection. Additionally, the efficiency of spatial query processing, and the accuracy of said results, can be problematic when applied to road networks. Existing approaches provide different online route APIs to deliver the precise POI, but mobile user demand not only Accurate, Efficient and Secure (AES) results, but results that do not threaten their privacy. In this paper, we have addressed these challenges by proposing an AES-Route Server (RS) approach for LBS, which supports common spatial queries, including Range Queries and k-Nearest Neighbor Queries. We can secure the user location through the proposed AES-RS model because it provides the query results accurate and efficiently. The proposed model satisfy the primary goals including accuracy, efficiency and privacy for a Location Base System.
Highlights
Recent years have witnessed the emergence of mobile computing technology as both a ubiquitous and extremely popular paradigm [1], wherein mobile users are capable of accessing information about nearby points-of-interest (POI)
These models are assorted into three basic categories including Untrusted Location Server (ULS), Trusted Location Server (TLS) and Peer to Peer based network (P2P) [29] where each model consist of three components as Mobile User Devices, Location Server and clients
By location privacy perspectives, we proposed AES-Route Server (RS) architecture which is an enhancement of RS algorithm and protect mobile user’s precise location information from any adversary
Summary
Recent years have witnessed the emergence of mobile computing technology as both a ubiquitous and extremely popular paradigm [1], wherein mobile users are capable of accessing information about nearby points-of-interest (POI). By geo-tagging a query, users are able to receive more personal, and valuable, results While helpful, this service depends on many factors, including Points-of-Interest, the precise information surrounding the user and their current location, and the inherent need for privacy protection [7]. Several challenges arise for spatial query processing, such as when there are multiple users and issuing the same query, q, for their POI, or when all mobile users belong to the same location In these scenarios, the server accrues additional overhead, and resources are wasted [3]. The primary goal of Route Server was to enhance the system efficiency with respect to query response time by reducing the number of route query requests They used upper and lower limit calculation approach for this purpose. Many privacy attacks were identified, effectively creating a taxonomy of attacks, and respective solutions were proposed, each with its advantages and disadvantages
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