The cloud-assisted Internet of Vehicles (IoV) enables vehicles to outsource computation-intensive tasks to remote cloud servers, while the introduction of untrusted cloud servers brings security and privacy concerns. Although various encryption schemes have been proposed to protect data confidentiality, they do not support selective computation over encrypted data. Multi-client functional encryption (MCFE) is a promising technique for computing functions over encrypted data. Unfortunately, most of the existing MCFE schemes cannot support client dropout or flexible threshold selection, which are essential for practical IoV deployments. In this paper, we design a flexible threshold multi-client functional encryption for inner product (FTMCFE-IP) to enable secure aggregation in cloud-assisted IoV, where multiple clients generate ciphertexts independently without any interaction. In the encryption phase, clients are able to choose a threshold flexibly without reinitializing the system. Every ciphertext is related with a specific label, which resists ”mix-and-match” attacks. The decryption can be performed correctly when the number of online clients satisfies the threshold. An aggregator can aggregate ciphertexts and output the inner product of the vectors associated with his/her functional key and the ciphertexts, respectively, but cannot learning anything else. Especially, the presented scheme supports clients drop out. We provide the definition and security model of our FTMCFE-IP scheme, and propose a concrete construction. The security of the designed scheme is formally proven under the SXDH assumption.
With the rapid development of 6G networks, the transmission of large-scale digital images faces severe security threats such as unauthorized interception and data leakage. Traditional encryption schemes and low-dimensional chaotic systems struggle to meet the demands for efficient, high-security image encryption in the 6G environment. To address this, this paper proposes a multi-image secure transmission scheme based on a novel NQA-4D hyperchaotic system and an adaptive Double Random Phase Encoding (DRPE) algorithm. Firstly, a new 4D hyperchaotic system with enhanced dynamical behavior is designed to expand the key space and improve chaotic properties. Secondly, a fusion method using exclusive quaternions and a 2D discrete Fourier transform is introduced to efficiently integrate information from multiple images. For diffusion, an adaptive genetic algorithm is employed, where crossover and mutation processes incorporate plaintext information, relative displacement, and iteration count to achieve encryption diversity and resist chosen-plaintext attacks. Experimental results demonstrate that the proposed scheme effectively ensures image security, enhances encryption and transmission efficiency, and is capable of meeting the secure transmission requirements for large-scale image data in the 6G era.
Electronic healthcare (E-health) cloud system leverages attribute-based encryption (ABE) to ensure electronic health records (EHRs) confidentiality and provide fine-grained access control for secure sharing and improved diagnostic efficiency. To prevent Economic Denial of Sustainability (EDoS) attacks from disrupting normal user download request access control mechanisms, the ABE-based dual access control scheme has been proposed. Unfortunately, existing ABE-based dual access control schemes still have the following defects: (1) no efficient way to locate encrypted EHRs stored in E-health cloud; (2) lack an effective malicious user tracking and revocation mechanism; (3) access control mechanism exposes sensitive patient information. In this paper, we tackle these challenges and present a privacy-preserving, traceable dual access control mechanism supporting multi-keyword search in E-Health cloud. We introduce keyword search to locate encrypted EHRs that doctors need to download. A white-box traceability and revocability mechanism is employed to trace malicious users and revoke their authorization. Patient privacy in policies is protected via a partially hidden policy, which reveals attribute names but keeps their values hidden. Experiments show our scheme offers comprehensive functionality with reasonable overhead.
Secure and reliable image transmission has become increasingly important for protecting confidential visual data. However, existing image encryption techniques still face challenges in achieving an effective balance between security, computational complexity, and robustness against various attacks. To address these limitations, we proposed a novel image encryption scheme that first employs the Secure Hash Algorithm (SHA-256) to generate a 240-bit master key. The two-dimensional Infinite Collapse Map (2D-ICM) is utilized to generate chaotic sequences for two-round permutation using the sub-keys derived from the master key. Subsequently, the permuted pixels are encoded into Deoxyribonucleic Acid (DNA) sequences and grouped into multiple sets, each containing four sequences. These sets undergo a unique DNA-based diffusion process achieving effective diffusion of all sequences at the nucleotide level. To ensure robust security, the cipher image is generated by applying XOR operations between the diffusion-processed sequences and the chaotic masked image. This scheme is evaluated on public datasets, including chest X-rays, fingerprints, the USC-SIPI database, and standard test images. The proposed scheme achieves an average entropy value of 7.997, NPCR of approximately 99.60%, and UACI close to the theoretical value of 33.46%, while maintaining low correlation coefficients and improved histogram uniformity. Moreover, robustness evaluations under noise and cropping attacks demonstrate reliable image recovery under transmission disturbances. The results indicate that the proposed approach provides an effective and efficient solution for secure grayscale image encryption.
