Preserving Location Privacy in the IoT against Advanced Attacks using Deep Learning

Abstract

Location-based services (LBSs) have received a significant amount of recent attention from the research community due to their valuable benefits in various aspects of society. In addition, the dependency on LBS in the performance of daily tasks has increased dramatically, especially after the spread of the COVID-19 pandemic. LBS users use their real location to build LBS queries to take benefits. This makes location privacy vulnerable to attacks. The privacy issue is accentuated if the attacker is an LBS provider since all information about users is accessible. Moreover, the attacker can apply advanced attacks, such as map matching and semantic location attacks. In response to these issues, this work employs artificial intelligence to build a robust defense against advanced location privacy attacks. The key idea behind protecting the location privacy of LBS users is to generate smart dummy locations. Smart dummy locations are false locations with the same query probability as the real location, but they are far from both the real location and each other. Relying on the previous two conditions, the deep-learning-based intelligent finder ensures a high level of location privacy protection against advanced attacks. The attacker cannot recognize the dummies from the real location and cannot isolate the real location by a filtering process. In terms of entropy (the privacy protection metric), accuracy (the deep learning metric), and total execution time (the performance metric) and compared to the well-known DDA and BDA systems, the proposed system shows better results, where entropy = 15.9, accuracy = 9.9, and total execution time = 17 sec.

A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges

“…Using location history and demographic features, Han et al 144 simulated a prediction framework and applying ML algorithms they detected possible risks and attacks from the published information of OSN users. Alyousef et al 145 proposed an intelligent location privacy scheme using deep learning and a set of dummy locations where attackers cannot distinguish the real location of a user. Rao et al 146 also proposed an ML‐based approach to store users' location information securely and they also proved the effectiveness of the approach by simulating two location privacy attacks.…”

Trust Management in Industrial Internet of Things using a Trusted E-Lithe Protocol

“…Along with the *Corresponding Author. www.ijacsa.thesai.org robustness and scalability requirements, the focus needs to be given to deploying fine-grained access control mechanisms [7].…”

“…According to Cisco and Gartner, currently, there are six billion users connected with the IoT devices; this number is increasing exponentially. It has been claimed by [7] that it is expected that IoT for industrial applications will help in improving security features and will offer great potential for research in this field in the coming years.…”

“…Both these factors are not acceptable for the efficient operation of any application. Thus, due to these issues, the questions stated below are developed to help direct research for bringing better prospects in deploying IoT in industrial applications and maintaining the trust factor [7]. b) If the performance impact is reduced, is it possible to increase interoperability?…”

“…To be able to answer these questions, a detailed analysis over energy, time consumption, and implementing trust factor in IIoT along with studying memory footprints and communication overheads, detailed analysis is conducted in coming sections of this research with the help of milestones depicted in Fig. 1 [7]. The methodology described for this research is based on an iterative process depicted in Fig.…”