Preserving Privacy in the Internet of Connected Vehicles

Abstract

Today's vehicles are advancing from stand-alone transportation means to vehicle-to-vehicle, and vehicle-toinfrastructure communications enabled devices which are able to exchange data through the transportation communication infrastructure. As the IoT and data remain intrinsically linked together, the fast-changing mobility landscape of intent-based networking for the Internet of connected vehicles comes with a great risk of data security and privacy violations. This paper considers the privacy issues in the distributed edge computing, in which the data is communicated between a number of vehicles in the IoT layer and potentially untrusted edge controllers at the edge of the network. The sensory data communicated by the vehicles contain sensitive information, such as location and speed, which could violate the users' privacy if they are leaked with no perturbation. Recent studies suggest mechanisms for randomizing the stream of data to ensure individuals' privacy. Although the past works on differential privacy provide a strong privacy guarantee, they are limited to applications where communication parties are trusted and/or there is no correlation between the users or the featured of sensory data. In this paper, we address this gap by proposing a differentially private data streaming system that adds a correlated noise in the vehicle's side (IoT layer) rather than the transportation infrastructure. Also, our system is able to ensure a strong privacy level over time. The proposed mechanism is data-adaptive and scales the noise with respect to the data correlation. Our extensive experiments demonstrate that the utility of the output generated by our method outperforms the recent approaches.Index Terms-Differential privacy, edge computing, intelligent transportation system, intent-based networking, Internet of connected vehicles.

Leveraging Intelligent Transportation Systems and Smart Vehicles Using Crowdsourcing: An Overview

“…In addition to E2E encryption, obfuscation techniques related to the location metadata of the data collection agent [42][43][44][45] are necessary when the data is being processed at the end of its journey through the communications network. These obfuscation techniques have also been explored in the edge as well [46].…”

“…Mobile Spatial Automated Sensing Task Recruitment [38,47] Advanced routing strategies beyond minimizing time traveled; powered through crowd-sourced data X X X [30] Spectrum sensing assignment X [11] Multi-objective task allocation X X [31] Ratings system for crowdsourced workers X [36] Spatial Crowdsourcing Task assignment survey X X [37] Crowdsourcing assignment for mobile crowdsensing X Edge Computing [33,48] Edge computing architectures for mobile crowdsensing X [10,49] Efficient data processing and handling on the edge X [27] Survey of IoT; discussion of edge computing X X X Security/Privacy [40,41] End-to-end encrypted mobile crowdsourcing X [42][43][44][45] Obfuscation of identifying information of crowdsourcing by reducing granularity of data X X X [46] Obfuscation of data in edge computing servers X [39] Perspectives and suggestions of engineering "privacy by design" in smart vehicles X…”

“…Security and privacy must be built into the system while it is being developed [39]. End-to-end encryption is utilized to safeguard communications in transit [40,41], and obfuscation methods are used to anonymize data during mission-critical processing tasks [42][43][44]46].…”

Privacy Challenges With Protecting Live Vehicular Location Context

“…DP techniques promise a rigorous level of privacy, however, there are limited applications that have adopted DP in practise. Some examples where DP has been proposed to be used include: scheduling the recharging of electronic vehicles [84] where the location of the vehicles are perturbed, vehicle platooning [85], and streaming data of multiple vehicles to Edge services [86].…”

A joint optimization scheme of content caching and resource allocation for internet of vehicles in mobile edge computing

“…If all such data is transmitted to the vehicle user by remote cloud networks, the vehicle user will experience a large delay. Due to vehicles switch between edge clouds frequently, vehicles need to constantly re-initiate requests to the cloud for obtaining the remaining data [1][2][3]. In addition, a single edge cloud-assisted caching strategy may cause vehicles to fail to obtain complete data in high-speed free-flow scenarios.…”