By leveraging the conventional Vehicular Ad-hoc Networks (VANETs), the Internet of Vehicles (IoV) paradigm has attracted the attention of different research and development bodies. However, IoV deployment is still at stake as many security and privacy issues are looming; location tracking using overheard safety messages is a good example of such issues. In the context of location privacy, many schemes have been deployed to mitigate the adversary’s exploiting abilities. The most appealing schemes are those using the silent period feature, since they provide an acceptable level of privacy. Unfortunately, the cost of silent periods in most schemes is the trade-off between privacy and safety, as these schemes do not consider the timing of silent periods from the perspective of safety. In this paper, and by exploiting the nature of public transport and role vehicles (overseers), we propose a novel location privacy scheme, called OVR, that uses the silent period feature by letting the overseers ensure safety and allowing other vehicles to enter into silence mode, thus enhancing their location privacy. This scheme is inspired by the well-known war strategy “Give up a Pawn to Save a Chariot”. Additionally, the scheme does support road congestion estimation in real time by enabling the estimation locally on their On-Board Units that act as mobile edge servers and deliver these data to a static edge server that is implemented at the cell tower or road-side unit level, which boosts the connectivity and reduces network latencies. When OVR is compared with other schemes in urban and highway models, the overall results show its beneficial use.

The quick revolution on the wireless communication technologies had opened the gate towards promising implementations; Vehicular-Ad-hoc Networks (VANETs) and the safety-enhancing applications provided by the Internet of Vehicles (IoV) paradigm are one of them. By periodically broadcasting safety-beacons, vehicles can ensure a better safety-driving experience since beacons contain fine-grained location that is sent to the neighborhood. Nevertheless, some attacks basing on falsify or encrypt location-related data are threatening the road-safety considerably. In this paper, and by assuming a GPS-spoofing attack originated from Unmanned-Aircraft-Vehicles (UAV) system, we provide a Security-Aware Monitoring Approach (SAMA) that protects vehicles against such location abusing by allowing the Law-Side Authority (LSA) to monitor the potential malicious or tricked vehicles. SAMA is Implemented using the triangulation concept via Received-Signal-Strength-Indicator (RSSI) in conjunction with C++ map and multimap data-structures. The performances of SAMA are evaluated in terms of location-estimation precision and beacons collection per type.

Internet of Vehicles (IoV) has the potential to enhance road-safety with environment sensing features provided by embedded devices and sensors. This benignant feature also raises privacy issues as vehicles announce their fine-grained whereabouts mainly for safety requirements, adversaries can leverage this to track and identify users. Various privacy-preserving schemes have been designed and evaluated, for example, mix-zone, encryption, group forming, and silent-period-based techniques. However, they all suffer inherent limitations. In this paper, we review these limitations and propose WHISPER, a safety-aware location privacy-preserving scheme that adjusts the transmission range of vehicles in order to prevent continuous location monitoring. We detail the set of protocols used by WHISPER, then we compare it against other privacy-preserving schemes. The results show that WHISPER outperformed the other schemes by providing better location privacy levels while still fulfilling road-safety requirements.

Internet of Vehicles (IoV) capabilities can be used to decrease the number of accidents by sharing information among entities like the location of the Smart Cars (SC). This information is not encrypted due to several real-time communications requirements. Many methods were proposed by the literature to withhold the attacker from exploiting such a privacy gap, affecting negatively at the same time other application layers like safety, comfort, and road-congestion. In this article, we provide a holistic overview of the effects of existing techniques on both privacy and other application layers both from the attacker and the defender point of view.

The Internet of Vehicles (IoV) has got the interest of different research bodies as a promising technology. IoV is mainly developed to reduce the number of crashes by enabling vehicles to sense the environment and spread their locations to the neighborhood via safety-beacons to enhance the system functioning. Nevertheless, a bunch of security and privacy threats are looming; by exploiting the spatio-data included in these beacons. A lot of privacy schemes were developed to cope with the problem like CAPS, CPN, RSP and SLOW. The schemes provide a certain level of location privacy yet the strength of the adversary, e.g., the number of eavesdropping stations, has not been fully considered. In this paper we aim at investigating the effect of the adversary’s eavesdropping stations number and position on the overall system functioning via privacy and QoS metrics. We also show the performances of these schemes in a manhattan-grid model which gives a comparison between the used schemes. The results show that both the number and the emplacement of the eavesdropping stations have a real negative impact on the achieved location privacy of the IoV users.

This paper presents a comprehensive survey of existing cyber security solutions for fog-based smart grid SCADA systems. We start by providing an overview of the architecture and the concept of fog-based smart grid SCADA systems and its main components. According to security requirements and vulnerabilities, we provide a classification of these solutions into four categories, including authentication solutions, privacy-preserving solutions, key management systems, and intrusion detection systems. For each category, we describe the essence of the methods and provide a classification with respect to security requirements. Therefore, according to the machine learning methods used by the intrusion detection system (IDS), we classify the IDS solutions into nine categories, including deep learning-based IDS, artificial neural networks-based IDS, support vector machine-based IDS, decision tree-based IDS, rule-based IDS, Bloom filter-based IDS, random forest-based IDS, random subspace learning-based IDS, and deterministic finite automaton-based IDS. The informal and formal security analysis techniques used by the cyber security solutions are tabulated and summarized. In addition, we provide a taxonomy of attacks tackled by privacy-preserving and authentication solutions in the form of tables. Based on the present study, several proposals for challenges and research issues such as detecting false data injection attacks are discussed at the end of the paper.

Internet of Vehicles (IoV) had remarkably enhanced the road -safety. Thanks to the environment sensing feature provided by the embedded devices and sensors. Nevertheless, this benignant feature had also introduced privacy issues; as vehicles spread their fine-grained locations at the aim of fulfilling safety requirements, adversaries can use this latter to track and identify the IoV users. Different privacy schemes and techniques are designed and evaluated like the mix-zone, encryption, groups forming, and silent-period based techniques. However, the majority do suffer from serious limitations inherited from the technique itself. In this paper, we propose a safety-friendly location privacy-preserving scheme, WHISPER, that adjusts the transmission range of vehicles on-the-fly in order to, occasionally, escape the continuous location tracking. We detail the protocols used by WHISPER, then we compare it against other privacy- preserving schemes using different metrics. The results show that WHISPER outperformed the other schemes after giving better location privacy levels while still keeping the road-safety fulfilled.

Vehicular Ad-hoc Networks (VANETs), which are a subclass of Mobile Ad-hoc Networks (MANETs), received a widespread attention during the last decades. With these promising set of safety applications, which are the main reason why they were developed, VANETs are considered as a tremendous support for the Intelligent Transportation Systems (ITS). However, several key issues remain to be solved before VANET becomes fully applicable; one of them being privacy preservation. To fulfill safety-requirements in VANETs, the vehicle needs to broadcast its status wirelessly. Consequently, any adversary can hear the broadcast messages at the aim of analyzing them, identifying, tracking and generating profile of his target. In other words, privacy of individuals may be seriously breached in VANETs if no safety measures were been taken. Using pseudonyms instead of the real identities of individuals, and changing them periodically during the communication is a promising solution for such crucial problems. There is a significant body of research work addressing this issue and a lot of researchers proposed various privacy protections basing on pseudonym change strategies. In this survey paper, we present an introduction to the privacy problem and give a recent and detailed state of the art of the different suggested pseudonym change strategies and approaches. We also propose a novel taxonomy to classify these strategies to diverse concepts. Finally, we discuss, give future directions and open issues and mention some of the observations that lead to better identify this problem for better future strategies.

The internet of vehicles (IoV) is getting a considerable amount of attention from different research parties. IoV aims at enhancing the driving experience with its wide range of applications varying from safety, road management to entertainment; however, some of such applications bring severe security and privacy issues; identity exposing, and location tracking are good examples. By enabling vehicles to send their statuses to themselves via beacon messages, this creates an environmental awareness for safety purposes but also exposes them to the aforementioned attacks. A lot of work has been done to mitigate the effect of such attacks but still does not provide a holistic solution. In this article, which is an extension to a prior work, the authors investigate the effects of changing the transmission range while sending beacons on the achieved level of location privacy based on two location privacy schemes: SLOW and CAPS. The authors use additional privacy metrics in addition to comparing the strategies in some well-known security attacks. The outcomes confirm the feasibility of using such a mechanism.

Vehicular Ad-hoc Networks (VANETs) are in the rollout phase. VANETs are mainly instantiated to mitigate the number of crashes and fatalities by enabling an intensive beaconing that contains the fine-grained location of each vehicle at the aim of creating environmental awareness for safety purposes. However, this frequent location information may be obtained by adversaries after overhearing the beacons; giving an unauthenticated entity(s) the ability to monitor the vehicles' whereabouts in a region of interest. A lot of schemes to protect location privacy were proposed. In this paper, we provide an enhancement of a set of schemes by allowing vehicles to adjust their beacon transmission range to conditionally avoid tracking. For the best of our knowledge, and excluding the scopes other than location privacy in VANETs, this is the first evaluation of transmission adjustment influence on the achieved location privacy. We made an evaluation of this feature's performances after integrating it into some of the well-known strategies, namely: SLOW and CAPS, against (1) the achieved location privacy using the traceability metric and (2) the network performances. The results show the beneficial usability of such a feature.