Smart meter privacy via the trapdoor channel

A battery charging policy that provides privacy guarantees for smart meter systems with finite capacity battery is proposed. For this policy an upper bound on the information leakage rate is provided. The upper bound applies for general random processes modelling the energy consumption of the user. It is shown that the average energy consumption of the user determines the information leakage rate to the utility provider. The upper bound is shown to be tight by deriving the probability law of a random process achieving the bound

Relationships between adolescent physical self-concept and physical activity

El objetivo de este estudio es someter a prueba dos modelos contrapuestos sobre las relaciones entre las autopercepciones físicas y la actividad físico-deportiva en la adolescencia: mientras que un modelo postula la influencia de la actividad física sobre el autoconcepto físico, el modelo alternativo propone al autoconcepto físico como factor influyente en la actividad físicodeportiva. Participan en la investigación 704 estudiantes, 394 (55.96 %) hombres y 310 (44.04 %) mujeres entre 11 y 19 años (M = 14.91; D.T. = 2.13), residentes en dos Comunidades Autónomas (Cantabria y País Vasco) de España. Los resultados indican que las influencias entre el autoconcepto físico y la actividad físico-deportiva se producen de forma bidireccional si bien ajusta mejor el modelo que propone al autoconcepto físico como factor influyente. Se comprueban diferencias entre hombres y mujeres en el modelo. Por otro lado, la autopercepción de atractivo físico mantiene una relación negativa con la actividad físico-deportivaThe aim of this study is to test two opposing models of the relationship between physical self-perceptions and physical activity during adolescence: one which postulates that physical activity influences physical self-concept, and another one which proposes that physical self-concept influences physical activity. Participants were 704 students aged between 11 and 19 (M = 14.91; SD = 2.13) from two different Autonomous Regions in Spain (Cantabria and the Basque Country). 394 (55.96%) were male and 310 (44.04%) were female. The results indicate that the influences between physical self-concept and physical activity are bidirectional in nature, although the model that proposes physical selfconcept as an influencing factor was found to have a better fitness. Differences were found in the model between male and female students. Furthermore, selfperception of physical attractiveness was found to be negatively related to physical activityEste artículo lo firman componentes del Grupo Consolidado de Investigación del Sistema Universitario Vasco IT701-13 y forman parte de los resultados del proyecto EDU2009-10102 (subprograma EDUC) subvencionado por el MICINN. La investigación se ha realizado con la colaboración del Programa para la Contratación de Doctores Recientes de la Universidad del País Vasco (UPV/EHU

Maximum Distortion Attacks in Electricity Grids

Multiple attacker data-injection attack construction in electricity grids with minimum-mean-square-error state estimation is studied for centralized and decentralized scenarios. A performance analysis of the trade-off between the maximum distortion that an attack can introduce and the probability of the attack being detected by the network operator is considered. In this setting, optimal centralized attack construction strategies are studied. The decentralized case is examined in a game-theoretic setting. A novel utility function is proposed to model this trade-off and it is shown that the resulting game is a potential game. The existence and cardinality of the corresponding set of Nash equilibria of the game is analyzed. Interestingly, the attackers can exploit the correlation among the state variables to facilitate the attack construction. It is shown that attackers can agree on a data-injection vector construction that achieves the best trade-off between distortion and detection probability by sharing only a limited number of bits offline. For the particular case of two attackers, numerical results based on IEEE test systems are presented

Information-theoretic attacks in the smart grid

Gaussian random attacks that jointly minimize the amount of information obtained by the operator from the grid and the probability of attack detection are presented. The construction of the attack is posed as an optimization problem with a utility function that captures two effects: firstly, minimizing the mutual information between the measurements and the state variables; secondly, minimizing the probability of attack detection via the Kullback-Leibler (KL) divergence between the distribution of the measurements with an attack and the distribution of the measurements without an attack. Additionally, a lower bound on the utility function achieved by the attacks constructed with imperfect knowledge of the second order statistics of the state variables is obtained. The performance of the attack construction using the sample covariance matrix of the state variables is numerically evaluated. The above results are tested in the IEEE 30-Bus test system

When Does Output Feedback Enlarge the Capacity of the Interference Channel?

In this paper, the benefits of channel-output feedback in the Gaussian interference channel (G-IC) are studied under the effect of additive Gaussian noise. Using a linear deterministic (LD) model, the signal to noise ratios (SNRs) in the feedback links beyond which feedback plays a significant role in terms of increasing the individual rates or the sum-rate are approximated. The relevance of this work lies on the fact that it identifies the feedback SNRs for which in any G-IC one of the following statements is true: (a) feedback does not enlarge the capacity region; (b) feedback enlarges the capacity region and the sum-rate is greater than the largest sum-rate without feedback; and (c) feedback enlarges the capacity region but no significant improvement is observed in the sum-rate

Recovery of missing data in correlated smart grid datasets

We study the recovery of missing data from multiple smart grid datasets within a matrix completion framework. The datasets contain the electrical magnitudes required for monitoring and control of the electricity distribution system. Each dataset is described by a low rank matrix. Different datasets are correlated as a result of containing measurements of different physical magnitudes generated by the same distribution system. To assess the validity of matrix completion techniques in the recovery of missing data, we characterize the fundamental limits when two correlated datasets are jointly recovered. We then proceed to evaluate the performance of Singular Value Thresholding (SVT) and Bayesian SVT (BSVT) in this setting. We show that BSVT outperforms SVT by simulating the recovery for different correlated datasets. The performance of BSVT displays the tradeoff behaviour described by the fundamental limit, which suggests that BSVT exploits the correlation between the datasets in an efficient manner

Stealth attacks on the smart grid

Random attacks that jointly minimize the amount of information acquired by the operator about the state of the grid and the probability of attack detection are presented. The attacks minimize the information acquired by the operator by minimizing the mutual information between the observations and the state variables describing the grid. Simultaneously, the attacker aims to minimize the probability of attack detection by minimizing the Kullback-Leibler (KL) divergence between the distribution when the attack is present and the distribution under normal operation. The resulting cost function is the weighted sum of the mutual information and the KL divergence mentioned above. The trade-off between the probability of attack detection and the reduction of mutual information is governed by the weighting parameter on the KL divergence term in the cost function. The probability of attack detection is evaluated as a function of the weighting parameter. A sufficient condition on the weighting parameter is given for achieving an arbitrarily small probability of attack detection. The attack performance is numerically assessed on the IEEE 14-Bus, 30-Bus, and 118-Bus test systems

Tracking with Sparse and Correlated Measurements via a Shrinkage-based Particle Filter

This paper presents a shrinkage-based particle filter method for tracking a mobile user in wireless networks. The proposed method estimates the shadowing noise covariance matrix using the shrinkage technique. The particle filter is designed with the estimated covariance matrix to improve the tracking performance. The shrinkage-based particle filter can be applied in a number of applications for navigation, tracking and localization when the available sensor measurements are correlated and sparse. The performance of the shrinkage-based particle filter is compared with the posterior Cramer-Rao lower bound, which is also derived in the paper. The advantages of the proposed shrinkage-based particle filter approach are demonstrated via simulation and experimental results

Robust recovery of missing data in electricity distribution systems

The advanced operation of future electricity distribution systems is likely to require significant observability of the different parameters of interest (e.g., demand, voltages, currents, etc.). Ensuring completeness of data is, therefore, paramount. In this context, an algorithm for recovering missing state variable observations in electricity distribution systems is presented. The proposed method exploits the low rank structure of the state variables via a matrix completion approach while incorporating prior knowledge in the form of second order statistics. Specifically, the recovery method combines nuclear norm minimization with Bayesian estimation. The performance of the new algorithm is compared to the information-theoretic limits and tested trough simulations using real data of an urban low voltage distribution system. The impact of the prior knowledge is analyzed when a mismatched covariance is used and for a Markovian sampling that introduces structure in the observation pattern. Numerical results demonstrate that the proposed algorithm is robust and outperforms existing state of the art algorithms

Recovering Missing Data via Matrix Completion in Electricity Distribution Systems

The performance of matrix completion based recovery of missing data in electricity distribution systems is analyzed. Under the assumption that the state variables follow a multivariate Gaussian distribution the matrix completion recovery is compared to estimation and information theoretic limits. The assumption about the distribution of the state variables is validated by the data shared by Electricity North West Limited. That being the case, the achievable distortion using minimum mean square error (MMSE) estimation is assessed for both random sampling and optimal linear encoding acquisition schemes. Within this setting, the impact of imperfect second order source statistics is numerically evaluated. The fundamental limit of the recovery process is characterized using Rate-Distortion theory to obtain the optimal performance theoretically attainable. Interestingly, numerical results show that matrix completion based recovery outperforms MMSE estimator when the number of available observations is low and access to perfect source statistics is not availabl