Browse Topic: Data privacy
Cybersecurity, particularly in the automotive sector, is of paramount importance in today’s digital age. With the advent of connected commercial vehicles, which leverage telematics for efficient fleet management, the landscape of automotive cybersecurity is rapidly evolving. These vehicles, integral to logistics and transportation businesses, are becoming increasingly connected, thereby escalating the risks associated with cybersecurity threats. These commercial vehicles are becoming prime targets for cyber-attacks due to their connectivity and the valuable data they hold. The potential consequences of these cyber-attacks can range from data breaches to disruptions in fleet operations, and even safety risks. This paper analyses the unique challenges faced by the commercial vehicle sector, such as the need for robust telematics systems, secure communication channels, and stringent data protection measures. Case studies of notable cybersecurity incidents involving commercial vehicles are
Data privacy questions are particularly timely in the automotive industry as—now more than ever before—vehicles are collecting and sharing data at great speeds and quantities. Though connectivity and vehicle-to-vehicle technologies are perhaps the most obvious, smart city infrastructure, maintenance, and infotainment systems are also relevant in the data privacy law discourse. Facial Recognition Software and Privacy Law in Transportation Technology considers the current legal landscape of privacy law and the unanswered questions that have surfaced in recent years. A survey of the limited recent federal case law and statutory law, as well as examples of comprehensive state data privacy laws, is included. Perhaps most importantly, this report simplifies the balancing act that manufacturers and consumers are performing by complying with data privacy laws, sharing enough data to maximize safety and convenience, and protecting personal information. Click here to access the full SAE EDGETM
The concerns surrounding AV adoption encompass the data protection factor. An online survey was conducted to gain insights into this concern, targeting UAE residents with knowledge about Autonomous Vehicle (AV) technology. The collected data were subjected to statistical analysis to provide valuable information for the UAE government and private sectors. To achieve this goal, we conducted a statistical analysis of the collected data, which resulted in further insights regarding the obstacles impeding the adoption of AV technologies in the United Arab Emirates. This analysis further quantifies the factors that contributed to UAE public concerns. We also examined user group evaluations in terms of their propensity to employ the technology in the future
By 2030, about 95% of new vehicles sold globally will be connected, up from around 50% today. Around 45% of these vehicles will have intermediate and advanced connectivity features (source: McKinsey, 2021). Modernization, standardization, and automation are the key steps in the roadmap of data handling for connected vehicles. Vehicle software increasingly sits within a connected ecosystem of devices. Consumer expectations are shifting more towards digital compatibility, connectivity, and new functionalities offered in autonomous vehicles. Digitalization is turning the vehicles of the future into commodities that are as experimental as they are useful. Many OEMs are at the beginning of this transformation journey and have struggled on the software side of things. The entire automotive industry is putting its efforts into effectively monetizing the data captured during the development and management of autonomous vehicles. It is not easy to handle the complexity, elasticity, and volume
Facial recognition software (FRS) is a form of biometric security that detects a face, analyzes it, converts it to data, and then matches it with images in a database. This technology is currently being used in vehicles for safety and convenience features, such as detecting driver fatigue, ensuring ride share drivers are wearing a face covering, or unlocking the vehicle. Public transportation hubs can also use FRS to identify missing persons, intercept domestic terrorism, deter theft, and achieve other security initiatives. However, biometric data is sensitive and there are numerous remaining questions about how to implement and regulate FRS in a way that maximizes its safety and security potential while simultaneously ensuring individual’s right to privacy, data security, and technology-based equality. Legal Issues Facing Automated Vehicles, Facial Recognition, and Individual Rights seeks to highlight the benefits of using FRS in public and private transportation technology and
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Even though ultrasound has been studied by scientists for many years, its capabilities in practical applications are yet to be fully harnessed
The connected car has already become a reality. It is a subject not just electrifying customers and manufacturers but also security researchers and IT experts. And in a worst-case scenario, criminal hackers as well. For years, security experts have observed the fact that the desktop PC is not the only target of digital attacks anymore. A large part of the malware is now customized to hit mobile devices. It would be negligent to believe that this development would leave the connected car unmolested
Autonomous vehicles might one day be able to implement privacy preserving driving patterns which humans may find too difficult to implement. In order to measure the difference between location privacy achieved by humans versus location privacy achieved by autonomous vehicles, this paper measures privacy as trajectory anonymity, as opposed to single location privacy or continuous privacy. This paper evaluates how trajectory privacy for randomized driving patterns could be twice as effective for autonomous vehicles using diverted paths compared to Google Map API generated shortest paths. The result shows vehicles mobility patterns could impact trajectory and location privacy. Moreover, the results show that the proposed metric outperforms both K-anonymity and KDT-anonymity
AN/GSN-5 mobile automatic landing system is a low approach and landing aid for modern aviation, providing capabilities for ground controlled talk-down approach, automatic or pilot-coupled low approach, and fully automatic landing for aircraft having suitable autopilots. Precise-tracking conical-scanning radar determines aircraft position and transfers data to computer which selects desired glide slope, glide angle, and determines altitude command as a function of range to touch-down. Alternate methods of transmission provided by GSN-5A - ILS and radar beam coding-are described. The beacon and decoder provide a system combining high tracking accuracy with a high capacity private data link
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