GeoSClean: Secure Cleaning of GPS Trajectory Data Using Anomaly Detection
Authors:
- Vikram PatilVPVikram Patil
- Priyanka SinghPSPriyanka Singh
- Shivam ParikhSPShivam Parikh
- Pradeep K. AtreyPAPradeep K. Atrey
- 1 April 2018
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 166-169
Abstract
Today cloud-based GPS enabled services or Location Based Services (LBS) are used more than ever because of a burgeoning number of smartphones and IoT devices and their uninterrupted connectivity to cloud. However, a number of hacking attacks on cloud raise serious security and privacy concerns among users; due to which many users do not like to share their location information. This poses a challenging problem of availing LBS from the cloud without revealing users location. Also, often GPS receivers record incorrect location data, which can affect the accuracy of LBS. In this paper, we propose a method, called GeoSClean, that not only cleans the GPS trajectory data using a novel anomaly detection scheme but also keeps users location confidential. Anomaly points are detected considering the combination of properties of the GPS trajectory data as distance, velocity, and acceleration. The experimental results validate the utility of the proposed method.Keywords
This publication has 12 references indexed in Scilit:
- GeoSecure: Towards secure outsourcing of GPS data over cloud2017
- Reversible data hiding for rightful ownership assertion of images in encrypted domain over cloudAEU - International Journal of Electronics and Communications, 2017
- Don't see me, just filter me2016
- Trajectory Data MiningACM Transactions on Intelligent Systems and Technology, 2015
- On detecting spatial categorical outliersGeoInformatica, 2013
- Trajectory Preprocessing2011
- A Fast Greedy Algorithm for Outlier Mining2006
- Algorithms for spatial outlier detection2004
- Self-aware services: using Bayesian networks for detecting anomalies in Internet-based services2002
- Local Indicators of Spatial Association—LISAGeographical Analysis, 1995