Cloud-fog-assisted electronic health record (EHR) systems offer promising solutions for large-scale medical data storage and processing. However, they also raise critical privacy concerns, particularly regarding secure computation over sensitive data, fine-grained bilateral access control, dynamic revocation, and decryption key exposure. Existing cryptographic primitives, such as functional encryption and matchmaking encryption, address some of these challenges individually but fail to offer a unified solution. In this work, we design a revocable and privacy-preserving data computing system with bilateral access control (RPDC-BAC) for cloud-fog-assisted EHR sharing by proposing a novel cryptographic primitive, called server-aided revocable attribute-based matchmaking functional encryption (SR-AB-MFE). Specifically, the proposed scheme supports expressive bilateral access control and computation over encrypted data. In addition, it incorporates time-evolving decryption keys and a server-aided revocation mechanism to mitigate key exposure and efficiently revoke users. To further reduce receiver-side overhead, fog nodes assist in ciphertext authentication and partial decryption. We formally define the proposed primitive and prove its security under static assumptions. Finally, extensive experimental results demonstrate the efficiency and practicality of our design.

The integration of cloud-edge computing with Internet of Things (IoT) healthcare greatly improves medical service efficiency and reduces home monitoring costs. However, in an untrusted and open environment, it still faces significant privacy and security challenges, especially in terms of confidentiality and authenticity of medical data, as well as bilateral access control between patients and healthcare providers. At present, there are few solutions capable of addressing the aforementioned issues efficiently, as they typically come with significant communication and computational overhead. This poses a substantial challenge for IoT devices that are usually resource-constrained. To address the above issues, this paper proposes an efficient and verifiable fine-grained bilateral access control scheme for cloud-edge IoT healthcare. The scheme adopts flexible attribute-based threshold bilateral access control to provide data confidentiality and authenticity at the same time. In addition, our scheme achieves constant-size ciphertexts, utilizes offline/online technology to accelerate ciphertext generation, and outsources the data authenticity verification and partial decryption process to edge nodes, thereby improving the communication and computational efficiency. Furthermore, our scheme implements verification of outsourced results to resist attacks from malicious edge nodes. The formal security proof and experimental evaluation show that our scheme is more functional and practical than other bilateral access control schemes for IoT healthcare.

MOTIVATION: The solvent accessible surface is an essential structural property measure related to the protein structure and protein function. Relative solvent accessible area (RSA) is a standard measure to describe the degree of residue exposure in the protein surface or inside of protein. However, this computation will fail when the residues information is missing. RESULTS: In this article, we proposed a novel method for estimation RSA using the Cα atom distance matrix with the deep learning method (EAGERER). The new method, EAGERER, achieves Pearson correlation coefficients of 0.921-0.928 on two independent test datasets. We empirically demonstrate that EAGERER can yield better Pearson correlation coefficients than existing RSA estimators, such as coordination number, half sphere exposure and SphereCon. To the best of our knowledge, EAGERER represents the first method to estimate the solvent accessible area using limited information with a deep learning model. It could be useful to the protein structure and protein function prediction. AVAILABILITYAND IMPLEMENTATION: The method is free available at https://github.com/cliffgao/EAGERER. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Vehicular ad hoc network (VANETs) plays a major part in intelligent transportation to enhance traffic efficiency and safety. Security and privacy are the essential matters needed to be tackled due to the open communication channel. Most of the existing schemes only provide message authentication without identity authentication, especially the inability to support forward secrecy which is a major security goal of authentication schemes. In this article, we propose a privacy-preserving mutual authentication scheme with batch verification for VANETs which support both message authentication and identity authentication. More importantly, the proposed scheme achieves forward secrecy, which means the exposure of the shared key will not compromise the previous interaction. The security proof shows that our scheme can withstand various known security attacks, such as the impersonation attack and forgery attack. The experiment analysis results based on communication and computation cost demonstrate that our scheme is more efficient compared with the related schemes.