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.

Homomorphic encryption (HE) is a promising technique for privacy-preserving computations, especially the word-wise HE schemes that allow batching. However, the high computational overhead hinders the deployment of HE in real-word applications. GPUs are often used to accelerate execution, but a comprehensive performance comparison of different schemes on the same platform is still missing. In this work, we fill this gap by implementing three word-wise HE schemes BGV, BFV, and CKKS on GPU, with both theoretical and engineering optimizations. We enhance the hybrid key-switching technique, significantly reducing the computational and memory overhead. We explore several kernel fusing strategies to reuse data, resulting in reduced memory access and IO latency, and enhancing the overall performance. By comparing with the state-of-the-art works, we demonstrate the effectiveness of our implementation. Meanwhile, we introduce a unified framework that finely integrates our implementation of the three schemes, covering almost all scheme functions and homomorphic operations. We optimize the management of pre-computation, RNS bases, and memory in the framework, to provide efficient and low-latency data access and transfer. Based on this framework, we provide a thorough benchmark of the three schemes, which can serve as a reference for scheme selection and implementation in constructing privacy-preserving applications.

Digital signatures are fundamental building blocks in various protocols to provide integrity and authenticity. The development of the quantum computing has raised concerns about the security guarantees afforded by classical signature schemes. CRYSTALS-Dilithium is an efficient post-quantum digital signature scheme based on lattice cryptography and has been selected as the primary algorithm for standardization by the National Institute of Standards and Technology. In this work, we present a high-throughput GPU implementation of Dilithium. For individual operations, we employ a range of computational and memory optimizations to overcome sequential constraints, reduce memory usage and IO latency, address bank conflicts, and mitigate pipeline stalls. This results in high and balanced compute throughput and memory throughput for each operation. In terms of concurrent task processing, we leverage task-level batching to fully utilize parallelism and implement a memory pool mechanism for rapid memory access. We propose a dynamic task scheduling mechanism to improve multiprocessor occupancy and significantly reduce execution time. Furthermore, we apply asynchronous computing and launch multiple streams to hide data transfer latencies and maximize the computing capabilities of both CPU and GPU. Across all three security levels, our GPU implementation achieves over 160× speedups for signing and over 80× speedups for verification on both commercial and server-grade GPUs. This achieves microsecond-level amortized execution times for each task, offering a high-throughput and quantum-resistant solution suitable for a wide array of applications in real systems.

In this paper, we introduce a new cryptographic primitive, called autonomous path proxy re-encryption (AP-PRE), which is motivated by several application scenarios where the delegator would like to control the whole delegation path in a multi-hop delegation process. Compared with the traditional proxy re-encryption, AP-PRE provides much better fine-grained access control to delegation path. Briefly speaking, in an AP-PRE scheme, the delegator designates a path of his preferred delegatees. The path consists of several delegatees with the privilege from high to low. If the delegatee in the path cannot complete the decryption, the decryption right is automatically delegated to the next one in the path. In this way, the delegator can ensure that the delegation has always been done among those delegatees the delegator trusts. Moreover, an AP-PRE scheme has to obey the following path rules. The delegation, for ciphertexts of a delegator i, can only be carried out on the autonomous path Pa _i designated by the delegator i, in the sense that (1) re-encrypted ciphertexts along the autonomous path Pa cannot branch off Pa with meaningful decryption, and (2) original ciphertexts generated under pk _j for j ≠ i (i.e., for a path Pa _j different from Pa _i ) cannot be inserted into (i.e., cannot be transformed along) the autonomous path Pa _i with meaningful decryption. We give the formal definition, as well as the formal security model, for this cryptographic primitive. Under this concept, we construct an IND-CPA secure AP-PRE scheme under the decisional bilinear DiffieHellman (DBDH) assumption in the random oracle model. Our scheme is with the useful properties of proxy re-encryption, i.e., unidirectionality and multi-hop.

Homomorphic encryption (HE), which allows computation over encrypted data, has often been used to preserve privacy. However, the computationally heavy nature and complexity of network topologies make the deployment of HE schemes in the Internet of Things (IoT) scenario difficult. In this work, we propose CARM, the first optimized GPU implementation that covers BGV, BFV and CKKS, targeting for accelerating homomorphic multiplication using GPU in heterogeneous IoT systems. Our solution is suitable for accelerating RNS homomorphic multiplication on both high-performance and embedded GPUs, as it is a parametric and generic design and offers various trade-offs between resource and efficiency. We offer constant-time low-level arithmetic with minimum instructions and memory usage, as well as performance- and memory-prior configurations. Through this, we can provide more real-time evaluation results and relieve the computational pressure on cloud devices. We deploy our implementations on two GPUs. Compared to the CPU implementation, we achieve up to $378.4\times$ , $234.5\times$ , and $287.2\times$ speedup for homomorphic multiplication of BGV, BFV, and CKKS on Tesla V100S, and $8.8\times$ , $9.2\times$ , and $10.3\times$ on Jetson AGX Xavier, respectively.