The widely use of IoT technologies in healthcare services has pushed forward medical intelligence level of services. However, it also brings potential privacy threat to the data collection. In healthcare services system, health and medical data that contains privacy information are often transmitted among networks, and such privacy information should be protected. Therefore, there is a need for privacy-preserving data collection (PPDC) scheme to protect clients (patients) data. We adopt (a,k)-anonymity model as privacy pretection scheme for data collection, and propose a novel anonymity-based PPDC method for healthcare services in this paper. The threat model is analyzed in the client-server-to-user (CS2U) model. On client-side, we utilize (a,k)-anonymity notion to generate anonymous tuples which can resist possible attack, and adopt a bottom-up clustering method to create clusters that satisfy a base privacy level of (a,k)-anonymity. On server-side, we reduce the communication cost through generalization technology, and compress (a,k)-anonymous data through an UPGMA-based cluster combination method to make the data meet the deeper level of privacy (a,k)-anonymity (a ≥ a, k ≥ k). Theoretical analysis and experimental results prove that our scheme is effective in privacy-preserving and data quality.
The adaptive hybrid function projective synchronization (AHFPS) of different chaotic systems with unknown time-varying parameters is investigated. Based on the Lyapunov stability theory and adaptive bounding technique, the robust adaptive control law and the parameters update law are derived to make the states of two different chaotic systems asymptotically synchronized. In the control strategy, the parameters need not be known throughly if the time-varying parameters are bounded by the product of a known function oftand an unknown constant. In order to avoid the switching in the control signal, a modified robust adaptive synchronization approach with the leakage-like adaptation law is also proposed to guarantee the ultimately uni-formly boundedness (UUB) of synchronization errors. The schemes are successfully applied to the hybrid function projective synchronization between the Chen system and the Lorenz system and between hyperchaotic Chen system and generalized Lorenz system. Moreover, numerical simulation results are presented to verify the effectiveness of the proposed scheme.
In order to further improve the accuracy and real-time performance of the traditional Single Shot Multibox Detector (SSD) object detection model, an improved SSD multi-object detection model is proposed. Firstly, aiming at the defect of weak correlation between prediction object score and positioning accuracy in the traditional SSD model, the improved model enhanced the correlation between the two by adding Intersection Over Union(IoU) prediction loss branch. Secondly, in order to reduce the spatial redundancy of traditional SSD model, a multi-frequency feature component convolution module is designed, which greatly reduces the calculation overhead and hardware overhead of the traditional model. Finally, in order to accelerate the convergence speed of the improved model, the Adaptive and Momental Bound (AdaMod) optimizer is introduced to modify the adaptive learning rate of the improved model which is too large in the training process. Experimental results show that the improved model has stronger detection capabilities, better overall detection results, and improved detection accuracy and real-time detection.
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