A Double Obfuscation Approach for Protecting the Privacy of IoT Location Based Applications

Abstract

Connected vehicles and smart cars have become highly reliant on location-based services (i.e. LBS) to provide accurate, personalized and intelligent services. However, location-based services have endangered its users to considerable risks concerning the privacy and security of users' personal data. Although existing research provides a myriad of methods to improve and protect user privacy in LBS applications, most of these methods are concerned with handling static queries and non-mobile objects only. Moreover, various issues and challenges still persist with regards to the need to trust third parties, overloading of the user, and low accuracy of the returned results. This paper contributes a Double Obfuscation Approach (referred to as DOA) that applies two phases of obfuscation consecutively whilst integrating two differing privacy protection approaches, namely Obfuscation and Trusted Third Party, and two techniques, namely fog caching technology and mix zone. In essence, the DOA obfuscates and hides the identity and location of its users using the fog nodes, which operate as a trusted third party (TTP), and without the need to reveal the identity of the users or trust the cooperating nodes. Moreover, this paper presents a DOA algorithm that improves the overall user privacy and system performance using the fog nodes, which split the responses of each query into five parts, thus reducing the processing time of the results by the user and enhancing the overall accuracy where the user directly selects the most suitable parts based on his current location. Overall, the hybrid DOA approach empowers the users of connected vehicle applications to protect their privacy through an algorithm that caters for the dynamic nature of user queries and mobility of objects. The results of our comparative simulations against well-known hybrid privacy protection methods demonstrate the superiority of the proposed Double Obfuscation Approach especially with respect to user privacy whilst maintaining a nominal overhead on the user, reduced response time and high accuracy of the obtained results.

A New Method With Swapping of Peers and Fogs to Protect User Privacy in IoT Applications

“…Here we provide a comparison of the SPF method with those which use a dummy (Enhance-Cache) [36], cooperation among peers P2PCache [45], encryption and TP (BTP) [33], and Double Obfuscation Approach DOA [34]. All of these approaches also use the cache technique.…”

“…Accordingly, advanced methods based on the trust of the SP should have an inbuilt system to alert users to grant permission for their data to be accessed [32]. How to avoid dependence on the trust of the SP has been an open problem, which has recently been addressed in the Blind Approach [33] by way of using a pair of keys in addition to the third party, and in the Double Obfuscation Approach (DOA) [34].…”

“…3) Double Obfuscation Approach [34] Purpose: Double Obfuscation Approach (DOA) is a recent hybrid method to protect the privacy of users in LBS applications. It depends on obfuscation and Fog as the third party (TP) to enhance privacy compared to the traditional obfuscation, and addresses some drawbacks related to overhead and accuracy of results in the Obfuscation Approach [37].…”

“…We shall not compare the SPF method with the Obfuscation approach, because of the fundamental difference in the nature of applications. Our comparison focuses on the Privacy, and Performance Metrics [16], [33], [34], [36], [45], [51], [53]. As described earlier, the Privacy Metric consists of 1) K-Anonymity (the percentage of the real queries of the user, to the K (queries) sent to the SP) 2) Entropy (the amount of true data out of the whole data received by the SP from the same user 3) Ubiquity (degree of the spread of the user in the study area)…”

Distributed Group Location Update Algorithm for Massive Machine Type Communication

“…Several studies were conducted on mobility management in IoT systems [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ]. These can be categorized into: (1) mobility management for an individual device [ 12 , 13 , 14 , 15 , 16 ]; (2) mobility management for a group of devices [ 17 , 18 , 19 , 20 , 21 , 22 , 23 ]; and (3) mobility management for the privacy of devices [ 24 , 25 , 26 , 27 ].…”