Indoor navigation systems are gaining traction due to their resistance to electromagnetic interference, abundant spectrum resources, and energy efficiency, underscoring the importance of indoor visible light positioning technology. Recent research focuses on using deep learning to enhance positioning accuracy, yet challenges remain in training costs, model efficiency, and performance in low Signal-to-Noise Ratio (SNR) scenarios. To address these issues, we propose a novel Long Short Term Memory network-Convolution Residual Network (LSTM-CRN) algorithm with a dataset construction method based on pilot extraction. Additionally, we introduce the Kolmogorov-Arnold Network (KAN) to improve accuracy under low SNR conditions. Extensive simulation results show that the network model trained on the dataset constructed by the pilot extraction method has higher localization efficiency and accuracy, especially compared with the network model trained directly using the received data to construct the dataset. The LSTM-KAN algorithm is trained on the dataset constructed by our method in this paper, and its average localization accuracy is verified to be 3.8 cm (SNR = 30). It also shows better localization accuracy, efficiency, and real-time performance than existing mainstream methods under different SNR conditions, proving that this method is the state-of-the-art in the system described in this article.

The popularization of intelligent healthcare devices and big data analytics significantly boosts the development of Smart Healthcare Networks (SHNs). To enhance the precision of diagnosis, different participants in SHNs share health data that contain sensitive information. Therefore, the data exchange process raises privacy concerns, especially when the integration of health data from multiple sources (linkage attack) results in further leakage. Linkage attack is a type of dominant attack in the privacy domain, which can leverage various data sources for private data mining. Furthermore, adversaries launch poisoning attacks to falsify the health data, which leads to misdiagnosing or even physical damage. To protect private health data, we propose a personalized differential privacy model based on the trust levels among users. The trust is evaluated by a defined community density, while the corresponding privacy protection level is mapped to controllable randomized noise constrained by differential privacy. To avoid linkage attacks in personalized differential privacy, we design a noise correlation decoupling mechanism using a Markov stochastic process. In addition, we build the community model on a blockchain, which can mitigate the risk of poisoning attacks during differentially private data transmission over SHNs. Extensive experiments and analysis on real-world datasets have testified the proposed model, and achieved better performance compared with existing research from perspectives of privacy protection and effectiveness.

Due to dramatically increasing information published in social networks, privacy issues have given rise to public concerns. Although the presence of differential privacy provides privacy protection with theoretical foundations, the trade-off between privacy and data utility still demands further improvement. However, most existing studies do not consider the quantitative impact of the adversary when measuring data utility. In this paper, we firstly propose a personalized differential privacy method based on social distance. Then, we analyze the maximum data utility when users and adversaries are blind to the strategy sets of each other. We formalize all the payoff functions in the differential privacy sense, which is followed by the establishment of a static Bayesian game. The trade-off is calculated by deriving the Bayesian Nash equilibrium with a modified reinforcement learning algorithm. The proposed method achieves fast convergence by reducing the cardinality from n to 2. In addition, the in-place trade-off can maximize the user’s data utility if the action sets of the user and the adversary are public while the strategy sets are unrevealed. Our extensive experiments on the real-world dataset prove the proposed model is effective and feasible.