The article investigates the problems of sensor fault estimation and fault-tolerant control for T-S fuzzy descriptor systems with disturbance. A novel regular form is devised for the fault estimation of T-S fuzzy descriptor systems, transforming the systems into a block structure. This transformation not only separates the observable and unobservable parts but also converts the singular matrix of the systems into a symmetric form, thereby simplifying the observer design and eliminating the equality constraint arising from the presence of the singular matrix. Subsequently, a controller in terms of the sliding mode observer is developed. Sufficient conditions are provided by the linear matrix inequality method for achieving simultaneous estimation of sensor faults and system states, as well as ensuring the stability of the overall closed-loop system. Furthermore, the sliding mode input of the sliding mode observer is designed to satisfy the reachability condition. Finally, three simulation examples are provided that illustrate the effectiveness of the proposed results.

This article studies the modeling method of false data injection attacks (FDIAs) considering topological changes and relevant countermeasures. A novel robust FDIA model is built, which incorporates network and measurement uncertainties into the attack subnet and can be applied to attack scenarios during topological changes. To tackle such FDIAs, a flexible dynamic time warping strategy-based distributed unsupervised detection mechanism is developed. Furthermore, an enhanced recognition model via hierarchical agglomerative clustering and local outlier factor techniques is proposed to facilitate operators to distinguish FDIAs. Compared with related works, the proposed model is more applicable to topological change scenarios and the detection framework can effectively discern such FDIAs in a distributed fashion. Simulation results demonstrate the stealthiness of the proposed FDIA model during and related to topological changes and the effectiveness of the distributed unsupervised detection method in tackling such attacks.

In this paper, we study the problem of publishing high-dimensional data in a distributed multi-party environment under differential privacy. In particular, with the assistance of a semi-trusted curator, the parties (i.e., local data owners) collectively generate a synthetic integrated dataset while satisfying ε-differential privacy. To solve this problem, we present a differentially private sequential update of Bayesian network (DP-SUBN) approach. In DP-SUBN, the parties and the curator collaboratively identify the Bayesian network N that best fits the integrated dataset in a sequential manner, from which a synthetic dataset can then be generated. The fundamental advantage of adopting the sequential update manner is that the parties can treat the intermediate results provided by previous parties as their prior knowledge to direct how to learn N. The core of DP-SUBN is the construction of the search frontier, which can be seen as a priori knowledge to guide the parties to update N. By exploiting the correlations of attribute pairs, we propose exact and heuristic methods to construct the search frontier. In particular, to privately quantify the correlations of attribute pairs without introducing too much noise, we first put forward a non-overlapping covering design (NOCD) method, and then devise a dynamic programming method for determining the optimal parameters used in NOCD. Through privacy analysis, we show that DP-SUBN satisfies ε-differential privacy. Extensive experiments on real datasets demonstrate that DP-SUBN offers desirable data utility with low communication cost.

In this paper, we study the novel problem of publishing high-dimensional data in a distributed multi-party environment under differential privacy. In particular, with the assistance of a semi-trusted curator, the involved parties (i.e., local data owners) collectively generate a synthetic integrated dataset while satisfying ε-differential privacy for any local dataset. To solve this problem, we present a differentially private sequential update of Bayesian network (DP-SUBN) solution. In DP-SUBN, the parties and the curator collaboratively identify the Bayesian network ℕ that best fits the integrated dataset D in a sequential manner, from which a synthetic dataset can then be generated. The fundamental advantage of adopting the sequential update manner is that the parties can treat the statistical results provided by previous parties as their prior knowledge to direct how to learn ℕ. The core of DP-SUBN is the construction of the search frontier, which can be seen as a priori knowledge to guide the parties to update ℕ. To improve the fitness of ℕ and reduce the communication cost, we introduce a correlation-aware search frontier construction (CSFC) approach, where attribute pairs with strong correlations are used to construct the search frontier. In particular, to privately quantify the correlations of attribute pairs without introducing too much noise, we first propose a non-overlapping covering design (NOCD) method, and then introduce a dynamic programming method to find the optimal parameters used in NOCD to ensure that the injected noise is minimum. Through formal privacy analysis, we show that DP-SUBN satisfies ε-differential privacy for any local dataset. Extensive experiments on a real dataset demonstrate that DP-SUBN offers desirable data utility with low communication cost.