With the substantial increase in image transmission, the demand for image security is increasing. Noise-like images can be obtained by conventional encrypt

Privacy-preserving localization plays a crucial role in enabling various applications on the Internet of Things (IoT). Existing work applies random zero-sum noise to develop privacy-preserving localization, which achieves efficiency and accuracy by adding random noise to preserve private information and cancelling the effect of the noise with the zero-sum characteristic, respectively. However, in practice, some nodes in IoT scenarios may misbehave, not following a pre-defined protocol but adding false noise or tampering with information, which leads to the trust issue for privacy-preserving localization. In this paper, we integrate blockchains with zero-sum noise to achieve trusted privacy-preserving localization against misbehaving nodes. Specifically, a three-layer framework is designed by combining private blockchains with a zero-sum noise-adding mechanism. In the sensing layer, nodes are divided into groups to perform the first noise-adding process to preserve their private location information during location aggregation inside the group. In the blockchain layer, each group constructs the private blockchains to achieve trustworthiness without increasing the risk of privacy leakage and performs the second noise-adding process to protect intermediate information. In the application layer, the target node aggregates the intermediate information from each group to estimate its location. Then, under the framework, we propose an Enhanced Privacy-Preserving Localization (EPPL) algorithm to securely calculate the location of the target node against misbehaving nodes. The correctness, accuracy, privacy, trustworthiness, and efficiency of EPPL are analyzed. The performance of EPPL is evaluated by using simulations. Compared with existing random noise-based methods, it is shown that EPPL can effectively enhance privacy-preserving localization.

Accurately predicting freeway accident severity is crucial for accident prevention, road safety, and emergency rescue services in intelligent freeway systems. However, current research lacks the required precision, hindering the effective implementation of freeway rescue. In this paper, we efficiently address this challenge by categorizing influencing factors into two levels: human and non-human, further subdivided into 6 and 36 categories, respectively. Furthermore, based on the above factors, an efficient and accurate Freeway Accident Severity Prediction (FASP) method is developed by using the two-level fuzzy comprehensive evaluation. The factor and evaluation sets are determined by calculating the fuzzy evaluation matrix of a single factor. The weight matrix is calculated through the entropy method to compute the final evaluation matrix. Based on the maximum membership principle, the severity of the freeway accident is predicted. Finally, based on the experiments conducted with the traffic accident datasets in China and the US, it is shown that FASP is able to accurately predict the severity of freeway traffic accidents with thorough considerations and low computational cost. It is noted that FASP is the first attempt to achieve freeway accident severity prediction using the two-level fuzzy comprehensive evaluation method to the best of our knowledge.