Measuring simultaneous belongingness for sets of objects

This paper presents the so‐called measures of simultaneous belongingness. These measures are used in clustering for establishing in which extent two objects belong to the same clusters. In the case of fuzzy clustering, the measure also takes into account fuzzy membership. In this paper, we establish a more general framework and, in particular, we introduce a definition that permits to compute this measure for sets of objects (instead of only to pairs of them). © 2008 Wiley Periodicals, Inc.     

Improving record linkage with supervised learning for disclosure risk assessment

In data privacy, record linkage can be used as an estimator of the disclosure risk of protected data. To model the worst case scenario one normally attempts to link records from the original data to the protected data. In this paper we introduce a parametrization of record linkage in terms of a weighted mean and its weights, and provide a supervised learning method to determine the optimum weights for the linkage process. That is, the parameters yielding a maximal record linkage between the protected and original data. We compare our method to standard record linkage with data from several protection methods widely used in statistical disclosure control, and study the results taking into account the performance in the linkage process, and its computational effort.     

A Zonotopic-Based Watermarking Design to Detect Replay Attacks

This paper suggests the use of zonotopes for the design of watermark signals. The proposed approach exploits the recent analogy found between stochastic and zonotopic-based estimators to propose a deterministic counterpart to current approaches that study the replay attack in the context of stationary Gaussian processes. In this regard, the zonotopic analogous case where the control loop is closed based on the estimates of a zonotopic Kalman filter (ZKF) is analyzed. This formulation allows to propose a new performance metric that is related to the Frobenius norm of the prediction zonotope. Hence, the steady-state operation of the system can be related with the size of the minimal Robust Positive Invariant set of the estimation error. Furthermore, analogous expressions concerning the impact that a zonotopic/Gaussian watermark signal has on the system operation are derived. Finally, a novel zonotopically bounded watermark signal that ensures the attack detection by causing the residual vector to exit the healthy residual set during the replay phase of the attack is introduced. The proposed approach is illustrated in simulation using a quadruple-tank process.      

An evolutionary approach to enhance data privacy

Dissemination of data with sensitive information about individuals has an implicit risk of unauthorized disclosure. Perturbative masking methods propose the distortion of the original data sets before publication, tackling a difficult tradeoff between data utility (low information loss) and protection against disclosure (low disclosure risk). In this paper, we describe how information loss and disclosure risk measures can be integrated within an evolutionary algorithm to seek new and enhanced masking protections for continuous microdata. The proposed technique constitutes a hybrid approach that combines state-of-the-art protection methods with an evolutionary algorithm optimization. We also provide experimental results using three data sets in order to illustrate and empirically evaluate the application of this technique.     

Adaptive threshold generation in robust fault detection using interval models: time‐domain and frequency‐domain approaches

In this paper, robust fault detection is addressed on the basis of evaluating the residual energy that it is compared against worst‐case value (threshold) generated considering parametric modeling uncertainty using interval models. The evaluation of the residual/threshold energy can be performed either in the time or frequency domain. This paper proposes methods to compute such energy in the two domains. The first method generates the adaptive threshold in the time domain through determining the worst‐case time evolution of the residual energy using a zonotope‐based algorithm. The second method evaluates the worst‐case energy evolution in the frequency domain using the Kharitonov polynomials. Results obtained using both approaches are related through the Parseval's theorem. Finally, two application examples (a smart servoactuator and a two DOFs helicopter) will be used to assess the validity of the proposed approaches and compare the results obtained. Copyright © 2012 John Wiley & Sons, Ltd.     

Selecting potentially relevant records using re-identification methods

This work proposes re-identification algorithms to select records that are interesting from the point of view of giving new information. Instead of focusing on re-identified elements, we focus on non re-identified records (non linked records) as they are the ones that potentially supply new and relevant information. Moreover, these relevant characteristics can correspond to chances for improving the knowledge of a system.To evaluate our approach, we have applied it to a example using publicly available data from the UCI repository. We have used the data of theionosphere data base to build a re-identification problem for 35 non-common variables.We show that the use of a simple heuristic rule base can effectively select potentially interesting records.