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J. Sens. Actuator Netw. 2016, 5(2), 10; doi:10.3390/jsan5020010

A Privacy-by-Design Contextual Suggestion System for Tourism

1
Electrical & Computer Engineering Department, Democritus University of Thrace, Xanthi 67100, Greece
2
School of Medicine, Democritus University of Thrace, Dragana, Alexandroupoli 68100, Greece
3
Information Technology Group, Wageningen University, Wageningen 6706 KN, The Netherlands
A preliminary version of this work appeared as “Pythia: A Privacy-Enhanced Personalized Contextual Suggestion System for Tourism. In the Proceedings of the 39th IEEE Annual International Computers, Software & Applications Conference (COMPSAC 2015), Taichung, Taiwan, 1–5 July 2015.
*
Author to whom correspondence should be addressed.
Academic Editor: Ioannis Chatzigiannakis
Received: 21 December 2015 / Revised: 21 April 2016 / Accepted: 22 April 2016 / Published: 5 May 2016
(This article belongs to the Special Issue Techno-Human Collective Systems for Smart Cities Innovation)
View Full-Text   |   Download PDF [6032 KB, uploaded 5 May 2016]   |  

Abstract

We focus on personal data generated by the sensors and through the everyday usage of smart devices and take advantage of these data to build a non-invasive contextual suggestion system for tourism. The system, which we call Pythia, exploits the computational capabilities of modern smart devices to offer high quality personalized POI (point of interest) recommendations. To protect user privacy, we apply a privacy by design approach within all of the steps of creating Pythia. The outcome is a system that comprises important architectural and operational innovations. The system is designed to process sensitive personal data, such as location traces, browsing history and web searches (query logs), to automatically infer user preferences and build corresponding POI-based user profiles. These profiles are then used by a contextual suggestion engine to anticipate user choices and make POI recommendations for tourists. Privacy leaks are minimized by implementing an important part of the system functionality at the user side, either as a mobile app or as a client-side web application, and by taking additional precautions, like data generalization, wherever necessary. As a proof of concept, we present a prototype that implements the aforementioned mechanisms on the Android platform accompanied with certain web applications. Even though the current prototype focuses only on location data, the results from the evaluation of the contextual suggestion algorithms and the user experience feedback from volunteers who used the prototype are very positive. View Full-Text
Keywords: privacy; personalization; contextual suggestion; privacy by design; non-invasiveness; tourism; mobile computing; recommendation systems privacy; personalization; contextual suggestion; privacy by design; non-invasiveness; tourism; mobile computing; recommendation systems
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Efraimidis, P.S.; Drosatos, G.; Arampatzis, A.; Stamatelatos, G.; Athanasiadis, I.N. A Privacy-by-Design Contextual Suggestion System for Tourism. J. Sens. Actuator Netw. 2016, 5, 10.

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