As cities become smarter, the importance of vehicular ad hoc networks (VANETs) will be increasingly pronounced. To support latency- and time-sensitive applications, there have been attempts to utilize fog-cloud computing in VANETs. There are, however, a number of limitations in existing fog-cloud based VANET deployments, ranging from computation and communication bottlenecks to privacy leakage to costly certificate/ pseudonym management to key escrow, and so on. Therefore, in this paper we propose a privacy-preserving aggregation authentication scheme (PPAAS). The scheme is designed for deployment in a safety warning system for fog-cloud based VANETs. Specifically, the PPAAS scheme is realized using a novel efficient anonymous certificateless aggregation signcryption scheme (CASS) proposed in this paper, and allows a fog node to aggregate signcrypted traffic-related messages from surrounding vehicles into an aggregated ciphertext and unsigncrypt them in a batch. We then evaluate the security of PPAAS and demonstrate that it supports confidentiality, authentication, and (efficient) conditional privacy, and key escrow freeness. In particular, our scheme is the first in the literature to achieve efficient conditional privacy, which avoids the need for costly pseudonym management. We also demonstrate that the scheme is practical, based on our simulation results.

Vehicular cloud (VC) extends cloud computing to vehicles participating in vehicular ad hoc networks, aiming to provide computing and storage services at low cost to vehicles, improve traffic efficiency and safety, ensure real-time services, etc. Due to the highly dynamic nature of VC, it is challenging to efficiently form a dynamic VC securely and anonymously or to securely deliver messages to the dynamic VC without potentially violating the privacy of cloud users. In this paper, we present a concrete secure and privacy-preserving communication scheme for VC establishment and data dissemination. Our scheme allows a group of vehicles that are geographically close to each other to form a VC securely, anonymously and dynamically. This allows vehicle resources to be integrated and shared securely. Once a VC is formed, any cloud user may deliver messages to be securely and anonymously processed in the VC.

While cloud computing is relatively mature and its potential benefits well understood by individual, industry and government consumers, a number of security and privacy concerns remain. Unsurprisingly, designing cryptographic solutions to ensure the security of cloud services and the privacy of data outsourced to the cloud remains an ongoing research area. This paper provides a critique of the wide range of cryptographic schemes designed for securing sensitive data in the cloud computing environment, as well as outlining the research opportunities in the use of cryptographic techniques in cloud computing.

Conventional federated learning (FL) approaches generally rely on a centralized server, and there has been a trend of designing asynchronous FL approaches for distributed applications partly to mitigate limitations associated with conventional (synchronous) FL approaches (e.g., single point of failure / attack). In this paper, we first introduce two new tools, namely: a quality-based aggregation method and an extended dynamic contribution broadcast encryption (DConBE). Building on these two new tools and local differential privacy, we then propose a privacy-preserving and reliable decentralized FL scheme, designed to support batch joining/leaving of clients while incurring minimal delay and achieving high model accuracy. In other words, our scheme seeks to ensure an optimal trade-off between model accuracy and data privacy, which is also demonstrated in our simulation results. For example, the results show that our aggregation method can effectively avoid low-quality updates in the sense that the scheme guarantees high model accuracy even in the presence of bad clients who may submit low-quality updates. In addition, our scheme incurs a lower loss and the extended DConBE only slightly affects the efficiency of our scheme. With the extended dynamic contribution broadcast encryption, our scheme can efficiently support batch joining/leaving of clients.

Fog computing is an extension of cloud computing and enables computing directly at the edge of the network. In fog computing paradigm, the fog nodes reside between smart end devices and the cloud. Benefiting from the structure of fog computing, fog computing can provide services with low latency, location awareness, and mobility. Since the fog nodes are not as powerful as the cloud, resource allocation techniques are usually adapted to optimize the utilization of the resources of fog nodes. However, the current resource allocation techniques are not privacy-preserving, i.e., an attacker can easily find end devices' sensitive information. In this paper, we propose a privacy-preserving resource allocation scheme for fog computing. The new proposal has constant message expansion, and it is secure against both an eavesdropper and a smart gateway that is employed to perform the resource allocation algorithm. Our scheme is also robust, since it achieves a full key compromise resistance which guarantees that even if the private keys of all the fog nodes in a fog system are corrupted the scheme remains secure.

The importance of designing efficient and secure vehicular ad hoc networks (VANETs), for example to address pressing traffic-related challenges in busy cities, is crucial but challenging. For example, existing approaches may suffer from vehicle-to-vehicle (V2V) communication-related limitations such as certificate management, key escrow, and performance. Seeking to contribute to the knowledge gap, we propose a new secure and privacy-preserving scheme for V2V communications. Our proposal is based on a new provably secure certificateless signature scheme with batch verification, which eliminates the need for certificates while ensuring better efficiency. Our performance and security evaluations demonstrate that the proposed approach overcomes our previously discussed limitations while achieving both message authentication and conditional privacy. In addition, we also shown that it incurs low overhead.