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With the advancement in technology, Cloud computing always amazes the world with revolutionizing solutions that automate and simplify complex computational tasks. The advantages like no maintenance cost, accessibility, data backup, pay-per-use models, unlimited storage, and processing power encourage individuals and businesses to migrate their workload to the cloud. Despite the numerous advantages of cloud computing, the geolocation of data in the cloud environment is a massive concern, which relates to the performance and government legislation that will be applied to data. The unclarity of data geolocation can cause compliance concerns. In this work, we have presented a technique that will allow users to restrict the geolocation of their data in the cloud environment. We have used trusted computing mechanisms to attest the host and its geolocation remotely. With this model, the user will upload the data whose decryption key will be shared with a third-party attestation server only. The decryption key will be sealed to the TPM of the host after successful attestation guaranteeing the authorized geolocation and platform state., Comment: Within couple of weeks the paper will be submitted to Journal of Information Security and Applications

The Internet of Vehicles (IoV) equips vehicles with connectivity to the Internet and the Internet of Things (IoT) to support modern applications such as autonomous driving. However, the consolidation of complex computing domains of vehicles, the Internet, and the IoT limits the applicability of tailored security solutions. In this paper, we propose a new methodology to quantitatively verify the security of single or system-level assets of the IoV infrastructure. In detail, our methodology decomposes assets of the IoV infrastructure with the help of reference sub-architectures and the 4+1 view model analysis to map identified assets into data, software, networking, and hardware categories. This analysis includes a custom threat modeling concept to perform parameterization of Common Vulnerability Scoring System (CVSS) scores per view model domain. As a result, our methodology is able to allocate assets from attack paths to view model domains. This equips assets of attack paths with our IoV-driven CVSS scores. Our CVSS scores assess the attack likelihood which we use for Markov Chain transition probabilities. This way, we quantitatively verify system-level security among a set of IoV assets. Our results show that our methodology applies to arbitrary IoV attack paths. Based on our parameterization of CVSS scores and our selection of use cases, remote attacks are less likely to compromise location data compared to attacks from close proximity for authorized and unauthorized attackers respectively.