This paper considers consensus-based distributed Kalman filtering subject to data-falsification attack, where Byzantine agents share manipulated data with their neighboring agents. The attack is assumed to be coordinated among the Byzantine agents and follows a linear model. The goal of the Byzantine agents is to maximize the network-wide estimation error while evading false-data detectors at honest agents. To that end, we propose a joint selection of Byzantine agents and covariance matrices of attack sequences to maximize the network-wide estimation error subject to constraints on stealthiness and the number of Byzantine agents. The attack strategy is then obtained by employing block-coordinate descent method via Boolean relaxation and backward stepwise based subset selection method. Numerical results show the efficiency of the proposed attack strategy in comparison with other naive and uncoordinated attacks.
This paper presents a private-partial distributed least mean square (PP-DLMS) algorithm that offers energy efficiency while preserving privacy and is suitable for applications with limited resources and strict security requirements. The proposed PP-DLMS allows every agent to exchange only a fraction of their perturbed data with neighbors during the collaboration process to minimize communication costs and guarantee privacy simultaneously. In order to understand how partial-sharing of perturbed data affects the learning performance, we conduct mean convergence analysis. Moreover, to investigate the privacypreserving properties of the proposed algorithm, we characterize agent privacy in the presence of an honest-but-curious (HBC) adversary. Analytical results show that the proposed PP-DLMS is resilient against an HBC adversary by providing a fair energyprivacy trade-off compared to the conventional LMS algorithm. Numerical simulations corroborate the analytical findings.
This paper proposes a privacy-preserving distributed Kalman filter (PP-DKF) to protect the private information of individual network agents from being acquired by honest-but-curious (HBC) adversaries. The proposed approach endows privacy by incorporating noise perturbation and state decomposition. In particular, the PP-DKF provides privacy by restricting the amount of information exchanged with decomposition and concealing private information from adversaries through perturbation. We characterize the performance and convergence of the proposed PP-DKF and demonstrate its robustness against perturbation. The resulting PP-DKF improves agent privacy, defined as the mean squared estimation error of private data at the HBC adversary, without significantly affecting the overall filtering performance. Several simulation examples corroborate the theoretical results.
Distributed Kalman filtering techniques enable agents of a multiagent network to enhance their ability to track a system and learn from local cooperation with neighbors. Enabling this cooperation, however, requires agents to share information, which raises the question of privacy. This paper proposes a privacy-preserving distributed Kalman filter (PP-DKF) that protects local agent information by restricting and obfuscating the information exchanged. The derived PP-DKF embeds two state-of-the-art average consensus techniques that guarantee agent privacy. The resulting PP-DKF utilizes noise injectionbased and decomposition-based privacy-preserving techniques to implement a robust distributed Kalman filtering solution against perturbation. We characterize the performance and convergence of the proposed PP-DKF and demonstrate its robustness against the injected noise variance. We also assess the privacy-preserving properties of the proposed algorithm for two types of adversaries, namely, an external eavesdropper and an honest-but-curious (HBC) agent, by providing bounds on the privacy leakage for both adversaries. Finally, several simulation examples illustrate that the proposed PP-DKF achieves better performance and higher privacy levels than the distributed Kalman filtering solutions employing contemporary privacy-preserving techniques.
By increase in smart phone penetration rate, mobile social networks (MSNs) become more popular. In such networks, users can exchange and share information via peer-to-peer opportunistic wireless connections. Wireless connections are prone to failures, devices are battery-powered, and the buffer space is limited. These lead to uncertainty in connections and selfish behaviours in dissemination processes. Hence, information dissemination in MSNs becomes a challenge. In this study, the authors analyse the information dissemination in MSNs with selfish users from different communities. They develop an analytical model through ordinary differential equations to analyse the dissemination process in MSNs. Then, they propose an optimisation problem to find the optimal forwarding probabilities of users. They employ the branch and bound-outer approximation algorithm to analytically solve the optimisation problem. The analytical results represent that the optimal forwarding probability of users diminished by increasing the number of relay users, which accelerate the dissemination in the network. Also, these results represent that the proposed algorithm to find the optimal selfishness vector can improve the network performance by decreasing the dissemination delay.
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