Existing ciphertext-policy attribute-based encryption (CP-ABE) schemes primarily determine who is authorized to decrypt, yet they do not guarantee privacy once ciphertexts are decrypted. Differential privacy (DP) protects released results through noise perturbation, but its privacy budget ε is usually configured independently of access attributes, which hinders fine-grained multi-level privacy protection in hierarchical IoT data sharing. To bridge this gap, a single-encryption multi-level output framework is proposed, where an attribute-driven noise key derivation function establishes a chained mapping among access attributes, noise keys, and noise intensity, enabling one ciphertext to yield differently perturbed outputs at distinct ε levels for users with varying privileges. Authenticated encryption with associated data (AEAD) is further incorporated to enforce strong cross-version and cross-policy binding, preventing low-noise outputs from being forged or replayed across authorization levels. Theoretical analysis proves that the framework achieves IND-CPA confidentiality, ε-differential privacy and tamper-resistant noise binding, while experiments demonstrate superior privacy–utility trade-offs, multi-level adaptability, and tamper resistance, indicating that the framework is well suited for practical IoT data sharing scenarios.

Cloud computing, as a promising service platform, has gained significant popularity in addressing emerging data privacy issues in applications such as machine learning and data mining. Researchers have proposed the verifiable computing that allows the cloud users to delegate their computation tasks to the cloud server. Then, the cloud server computes the cryptographic proofs that verify the correctness of the results, a process that is generally faster ompared to local manual computation. However, performing computation tasks or verifying the correctness of encrypted data, such as multivariate polynomial functions, remains a significant challenge. To solve this problem, we propose Einocchio: a verifiable computation scheme that combines the efficient Pinocchio system with homomorphic encryption, which allows the public verification of the computational results on the server side while ensuring data confidentiality and the results. Compared with the existing solutions, Einocchio does not reveal the client’s input. Furthermore, we extrapolate Einocchio by optimizing the Pinocchio’s quadratic arithmetic program component using a differential optimization method, which reduces the computational workload owing to the conversion from quadratic to linear complexity, thereby increasing the efficiency of the quadratic arithmetic program preprocessing stage. Security analysis demonstrates that Einocchio achieves IND-CPA security. Finally, the performance evaluation confirmed its effectiveness and suitability for cloud computing environments. Compared to the corresponding scheme based on Newton interpolation, Einocchio achieves a threefold greater computational efficiency, with the generation of interpolation polynomials for 50 data inputs occurring in a mere 0.31 ms, while simultaneously reducing the number of computations.

The Internet of Vehicles (IoV) is a specific instance of the Internet of Things (IoT) in the transportation field, driven by application requirements, such as intelligent traffic services and automatic vehicle control, can improve road safety and enhancing transmission efficiency. However, highly open networks tend to bring more security threats, and secure authentication becomes an important guarantee for reliable communication. Traditional IoT authentication and key agreement methods are costly, inefficient, and rely on the third-party trusted institutions, making them unsuitable for direct application in IoV systems. To meet the security authentication needs of IoV, and improve authentication efficiency and anonymity, this article proposes a verifiable commitment-based mutual authentication and key agreement protocol, called mutual authentication and key agreement protocol based on verifiable commitment (VC-MAKA). In VC-MAKA, we construct a verifiable commitment scheme where the verifier can verify the committed secret. Furthermore, based on this verifiable commitment scheme, we implement secure authentication and session key agreement, allowing vehicles to freely negotiate secure session keys and achieving conditional anonymous protection. Additionally, the proposed VC-MAKA also achieves rapid session key updates, enhancing the security of the session keys. We have conducted formal and informal security analysis, and the results show that VC-MAKA meets security requirements, such as mutual authentication, anonymity, traceability, and untraceability. Moreover, we have used the ProVerif tool for security experiment and performance comparison analysis, and the results indicate that compared to other schemes, the VC-MAKA protocol offers higher security and better efficiency.

Fair data trading is a complex process that is often hindered by a fundamental issue of trust between data suppliers and collectors. This mistrust can lead to an impasse: data collectors hesitate to pay upfront without the data in hand, while data suppliers hold back the data until they are assured of payment. Though enlisting a trusted third party may mitigate these issues, it also presents distinct security challenges that must be carefully considered. Observing that the blockchain technique has great potential to improve security, efficiency, and transparency of data trading, we present a blockchain-based fair data trading scheme, called BFFDT, which allows the data seller trade its data in part with an interested purchaser through a smart contract for revenue. In BFFDT, the data publisher first generates the authenticated tags based on the data fields and corresponding attribute values, then encrypts the corresponding attribute values individually and generates a dynamic Merkle hash tree (D-MHT) to ensure the consistency of the attributes and attribute values. In addition, we design an innovative pairing-based proxy re-encryption mechanism to transmit the ciphertext of a symmetric key to the purchaser’s public key via a re-encryption key without any third-party intermediary, and verifies the re-encryption key using the verifiable commitment. Furthermore, the BFFDT is formally proven to be secure against the deceitful actions of both the fraudulent seller and buyer, and the experimental outcomes further confirm that BFFDT offers high efficiency and practical applicability.

In Industry 5.0, there are increasing demands for group communication with low energy consumption and high communication efficiency from a great number of Internet of Things (IoT) devices. However, group communication is still exposed to various security risks. Although some cryptographic schemes have been devised to facilitate secure group communication, the existing schemes generally rely on a trust authority to periodically issue certificates and have led to various issues, such as failing to support anonymity and flexible key management, and cannot resist the single point of failure. Therefore, in this work, leveraging Chebyshev chaotic maps and blockchain, an anonymous authentication and key management scheme is proposed to provide secure and efficient group key generation and management for mutual authentication between communication entities. The scheme exploits the blockchain to save the key materials associated with IoT devices, thereby it ensures data privacy and provides a secure environment for communication. The scheme also employs the Chebyshev polynomial to generate a group key for the IoT devices within a group, and later the group members holding the same group key can use it for secure communication. The formal and informal security analysis demonstrates that the proposed scheme can meet the security and flexible key management requirements. The detailed performance analysis shows that the proposed scheme has acceptable computation and communication energy consumption and provides superior security in comparison with existing schemes.

Drug discovery is a lengthy, costly and high-risk endeavour that is further convoluted by high attrition rates in later development stages. Toxicity has been one of the main causes of failure during clinical trials, increasing drug development time and costs. To facilitate early identification and optimisation of toxicity profiles, several computational tools emerged aiming at improving success rates by timely pre-screening drug candidates. Despite these efforts, there is an increasing demand for platforms capable of assessing both environmental as well as human-based toxicity properties at large scale. Here, we present toxCSM, a comprehensive computational platform for the study and optimisation of toxicity profiles of small molecules. toxCSM leverages on the well-established concepts of graph-based signatures, molecular descriptors and similarity scores to develop 36 models for predicting a range of toxicity properties, which can assist in developing safer drugs and agrochemicals. toxCSM achieved an Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of up to 0.99 and Pearson's correlation coefficients of up to 0.94 on 10-fold cross-validation, with comparable performance on blind test sets, outperforming all alternative methods. toxCSM is freely available as a user-friendly web server and API at http://biosig.lab.uq.edu.au/toxcsm.