An Anti-Furthest Neighbor Verification Method in Outsourced Spatial Database

Abstract

The invention discloses an anti-farthest neighbor verification method suitable for outsourced spatial databases. Based on the field of location services and spatiotemporal data management, it ensures the correctness of the client's acceptance of the anti-farthest neighbor result set. The overall processing is divided into third-party server processing and There are two parts to client verification. Based on the MR‑Tree index structure, an optimization algorithm for reducing verification objects is proposed to reduce costs such as communication and client verification. The steps for the server to respond to the request: determine whether the request is valid; use half-plane pruning technology to generate an Influenced Zone for valid queries; use the range query verification idea to form a result set and its VO; send the result set, VO and Influenced Zone to the client. Client verification steps: Use VO to determine whether the data is reliable; filter the data through the Influenced Zone and compare it with the result set; if consistent, the verification is successful and the client accepts the result set. The algorithm of the present invention is applied to RkFN query verification based on location services in massive data, and has high efficiency and wide application range.

Description

Technical field [0001] The invention belongs to the combined field of spatio-temporal data management and location-based services, and is an algorithm suitable for the client to perform query and verification based on location services in an outsourcing spatial database. Background technique [0002] Nowadays, humans have increasingly strong demand for location-based services (LBS), such as map navigation, finding the k nearest coffee shops, etc. (ie: nearest neighbor query kNN); the driver sends out a message to find the nearest k refueling The gas station uses the monitoring system to reversely screen out k nearest neighbor drivers. If the driver can become one of the k nearest neighbors, the gas station will prepare in advance to provide better services (ie: anti-nearest neighbor Check RkNN); in military exercises or actual combat, military deployments often ignore the characteristics of the farthest enemy that is most vulnerable to attack. Due to the long distance and the low...

AI Technical Summary

Problems solved by technology

[0006] The sources of wrong information obtained by users mainly include data loss during transmission, hackers attacking servers and tampering with their data, etc. Passive acceptance of information by users may cause users to deviate from their goals under the guidance of wrong information, or provide a non-optimal destination. In view of this situation, how should the client design the verification method to achieve the purpose of verifying the result? This is a necessary issue that we need to consider

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