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In this paper, we present a secure distributed estimation strategy in networked systems. In particular, we consider distributed Kalman filtering as the estimation method and Paillier encryption, which is a partially homomorphic encryption scheme. The proposed strategy protects the confidentiality of the transmitted data within a network. Moreover, it also secures the state estimation computation process. To this end, all the algebraic calculations needed for state estimation in a distributed Kalman filter are performed over the encrypted data. As Paillier encryption only deals with integer data, in general, this, in turn, provides significant quantization error in the computation process associated with the Kalman filter. However, the proposed estimation approach handles quantized data in an efficient way. We provide an optimality and convergence analysis of our proposed method. It is shown that state estimation and a covariance matrix associated with the proposed method remain with a certain small radius of those of a conventional centralized Kalman filter. Simulation results are given to further demonstrate the effectiveness of the proposed scheme.  相似文献   
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This article is the first in the feature to come from Iran. The lead author, Sara Ahmadizadeh, manages the Library at Arash Women's Hospital and was awarded an MA in Information Science and Knowledge and Librarianship from Tehran Azad University in July 2015. Her MA dissertation was innovative and ambitious in that it looked at the impact of information therapy on levels of anxiety amongst women undergoing IVF treatment. Her study demonstrated that quality information sessions, delivered in a structured way, can have a positive impact on patients’ awareness of their condition and can reduce anxiety. A. M.  相似文献   
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