ePRIVO: an enhanced PRIvacy-preserVing opportunistic routing protocol for vehicular delay-tolerant networks

This article proposes an enhanced PRIvacy preserVing Opportunistic routing protocol (ePRIVO) for Vehicular Delay-Tolerant Networks (VDTN). ePRIVO models a VDTN as a time-varying neighboring graph where edges correspond to neighboring relationship between pairs of vehicles. It addresses the problem of vehicles taking routing decision meanwhile keeping their information private, i.e, vehicles compute their similarity and/or compare their routing metrics in a private manner using the Paillier homomorphic encryption scheme. The effectiveness of ePRIVO is supported through extensive simulations with synthetic mobility models and a real mobility trace. Simulation results show that ePRIVO presents on average very low cryptographic costs in most scenarios. Additionally, ePRIVO presents on average gains of approximately 29% and 238% in terms of delivery ratio for the real and synthetic scenarios considered compared to other privacy-preserving routing protocols

A Taxonomy on Misbehaving Nodes in Delay Tolerant Networks

Delay Tolerant Networks (DTNs) are type of Intermittently Connected Networks (ICNs) featured by long delay, intermittent connectivity, asymmetric data rates and high error rates. DTNs have been primarily developed for InterPlanetary Networks (IPNs), however, have shown promising potential in challenged networks i.e. DakNet, ZebraNet, KioskNet and WiderNet. Due to unique nature of intermittent connectivity and long delay, DTNs face challenges in routing, key management, privacy, fragmentation and misbehaving nodes. Here, misbehaving nodes i.e. malicious and selfish nodes launch various attacks including flood, packet drop and fake packets attack, inevitably overuse scarce resources (e.g., buffer and bandwidth) in DTNs. The focus of this survey is on a review of misbehaving node attacks, and detection algorithms. We firstly classify various of attacks depending on the type of misbehaving nodes. Then, detection algorithms for these misbehaving nodes are categorized depending on preventive and detective based features. The panoramic view on misbehaving nodes and detection algorithms are further analyzed, evaluated mathematically through a number of performance metrics. Future directions guiding this topic are also presented

User-centric privacy preservation in Internet of Things Networks

Recent trends show how the Internet of Things (IoT) and its services are becoming more omnipresent and popular. The end-to-end IoT services that are extensively used include everything from neighborhood discovery to smart home security systems, wearable health monitors, and connected appliances and vehicles. IoT leverages different kinds of networks like Location-based social networks, Mobile edge systems, Digital Twin Networks, and many more to realize these services. Many of these services rely on a constant feed of user information. Depending on the network being used, how this data is processed can vary significantly. The key thing to note is that so much data is collected, and users have little to no control over how extensively their data is used and what information is being used. This causes many privacy concerns, especially for a na ̈ıve user who does not know the implications and consequences of severe privacy breaches. When designing privacy policies, we need to understand the different user data types used in these networks. This includes user profile information, information from their queries used to get services (communication privacy), and location information which is much needed in many on-the-go services. Based on the context of the application, and the service being provided, the user data at risk and the risks themselves vary. First, we dive deep into the networks and understand the different aspects of privacy for user data and the issues faced in each such aspect. We then propose different privacy policies for these networks and focus on two main aspects of designing privacy mechanisms: The quality of service the user expects and the private information from the user’s perspective. The novel contribution here is to focus on what the user thinks and needs instead of fixating on designing privacy policies that only satisfy the third-party applications’ requirement of quality of service

Towards efficacy and efficiency in sparse delay tolerant networks

The ubiquitous adoption of portable smart devices has enabled a new way of communication via Delay Tolerant Networks (DTNs), whereby messages are routed by the personal devices carried by ever-moving people. Although a DTN is a type of Mobile Ad Hoc Network (MANET), traditional MANET solutions are ill-equipped to accommodate message delivery in DTNs due to the dynamic and unpredictable nature of people\u27s movements and their spatio-temporal sparsity. More so, such DTNs are susceptible to catastrophic congestion and are inherently chaotic and arduous. This manuscript proposes approaches to handle message delivery in notably sparse DTNs. First, the ChitChat system [69] employs the social interests of individuals participating in a DTN to accurately model multi-hop relationships and to make opportunistic routing decisions for interest-annotated messages. Second, the ChitChat system is hybridized [70] to consider both social context and geographic information for learning the social semantics of locations so as to identify worthwhile routing opportunities to destinations and areas of interest. Network density analyses of five real-world datasets is conducted to identify sparse datasets on which to conduct simulations, finding that commonly-used datasets in past DTN research are notably dense and well connected, and suggests two rarely used datasets are appropriate for research into sparse DTNs. Finally, the Catora system is proposed to address congestive-driven degradation of service in DTNs by accomplishing two simultaneous tasks: (i) expedite the delivery of higher quality messages by uniquely ordering messages for transfer and delivery, and (ii) avoid congestion through strategic buffer management and message removal. Through dataset-driven simulations, these systems are found to outperform the state-of-the-art, with ChitChat facilitating delivery in sparse DTNs and Catora unencumbered by congestive conditions --Abstract, page iv

Efficient location privacy-aware forwarding in opportunistic mobile networks

This paper proposes a novel fully distributed and collaborative k-anonymity protocol (LPAF) to protect users’ location information and ensure better privacy while forwarding queries/replies to/from untrusted location-based service (LBS) over opportunistic mobile networks (OppMNets. We utilize a lightweight multihop Markov-based stochastic model for location prediction to guide queries toward the LBS’s location and to reduce required resources in terms of retransmission overheads. We develop a formal analytical model and present theoretical analysis and simulation of the proposed protocol performance. We further validate our results by performing extensive simulation experiments over a pseudo realistic city map using map-based mobility models and using real-world data trace to compare LPAF to existing location privacy and benchmark protocols. We show that LPAF manages to keep higher privacy levels in terms of k-anonymity and quality of service in terms of success ratio and delay, as compared with other protocols, while maintaining lower overheads. Simulation results show that LPAF achieves up to an 11% improvement in success ratio for pseudorealistic scenarios, whereas real-world data trace experiments show up to a 24% improvement with a slight increase in the average delay