Stojan Z. Denic

Privacy for Smart Meters: Towards Undetectable Appliance Load Signatures

Smart grid privacy encompasses the privacy of information extracted by analysing smart metering data. In this paper, we suggest that home electrical power routing can be used to moderate the homes load signature in order to hide appliance usage information. In particular, 1) we introduce a power management model using a rechargeable battery, 2) we propose a power mixing algorithm, and 3) we evaluate its protection level by proposing three different privacy metrics: an information theoretic (relative entropy), a clustering classification, and a correlation/regression one; these are tested on different metering datasets. This paper sets the ground for further research on the subject of optimising home energy management with regards to hiding load signatures.

Penetration of the stigma and style elicits a novel transcriptome in pollen tubes, pointing to genes critical for growth in a pistil.

Pollen tubes extend through pistil tissues and are guided to ovules where they release sperm for fertilization. Although pollen tubes can germinate and elongate in a synthetic medium, their trajectory is random and their growth rates are slower compared to growth in pistil tissues. Furthermore, interaction with the pistil renders pollen tubes competent to respond to guidance cues secreted by specialized cells within the ovule. The molecular basis for this potentiation of the pollen tube by the pistil remains uncharacterized. Using microarray analysis in Arabidopsis, we show that pollen tubes that have grown through stigma and style tissues of a pistil have a distinct gene expression profile and express a substantially larger fraction of the Arabidopsis genome than pollen grains or pollen tubes grown in vitro. Genes involved in signal transduction, transcription, and pollen tube growth are overrepresented in the subset of the Arabidopsis genome that is enriched in pistil-interacted pollen tubes, suggesting the possibility of a regulatory network that orchestrates gene expression as pollen tubes migrate through the pistil. Reverse genetic analysis of genes induced during pollen tube growth identified seven that had not previously been implicated in pollen tube growth. Two genes are required for pollen tube navigation through the pistil, and five genes are required for optimal pollen tube elongation in vitro. Our studies form the foundation for functional genomic analysis of the interactions between the pollen tube and the pistil, which is an excellent system for elucidation of novel modes of cell–cell interaction.

ElecPrivacy: Evaluating the Privacy Protection of Electricity Management Algorithms

The data collected by a home smart meter can potentially reveal sensitive private information about the home resident(s). In this paper, we study how home energy resources can be used to protect the privacy of the collected data. In particular we: a) introduce a power mixing algorithm to selectively protect a set of consumption events; b) develop a range of different privacy protection metrics; c) analyze real smart metering data sampled twice a minute over a period of 13 days; and d) evaluate the protection offered by different power mixing algorithms. Major factors which determine the efficiency of the proposed power mixing algorithms are identified, such as battery capacity and power, and user preferences for privacy-based allocations of battery energy quotas.

Information Theoretic Limits for Free-Space Optical Channels With and Without Memory

The availability of Channel State Information (CSI) and the effects of channel memory on the capacities and the achievable rates of free-space optical communication channels are investigated. For memoryless channels, the capacities and achievable rates are computed and compared for both uniform and ldquopositiverdquo Gaussian inputs subject to different assumptions on the CSI availability. For the strong turbulence regime, it is shown that the knowledge of CSI both at the transmitter and the receiver increases the achievable rates for low-to-moderate Signal-to-Noise Ratios (SNRs) in comparison to the cases for which the CSI is known only at the receiver. For the weak turbulence regime however, the availability of CSI at both ends of the link does not provide any improvement over a system with CSI known at the receiver alone, and we find that a simple channel inversion technique suffices. In addition, for low SNRs, Pulse Amplitude Modulation (PAM) with M ges 4 levels outperforms Gaussian-distributed inputs regardless of the knowledge of CSI at the transmitter. For high SNRs, a Gaussian distribution gives superior results, implying the need for new, more efficient positive signal constellations. For channels with memory and without knowledge of CSI, a change in the channel quasifrequency has negligible effects on the capacity for any turbulence regime.

Privacy protection system and metrics for hiding electrical events

Smart grid privacy concerns the privacy of information extracted by analysing smart metering data. We present ElecPrivacy: a home electrical power management system that uses a rechargeable battery to mask home energy load signatures and, effectively, protect the privacy of appliance usage information. ElecPrivacy can be studied in the context of the classic communications problem, where input data is passed through a communication channel that distorts it. In this paper, we define and measure how the appearance of ElecPrivacy events can be estimated, or, reversely, how well the secrecy of this data is protected. In particular, we develop a range of privacy metrics by combining clustering, information theoretic (K-divergence), correlation and regression techniques, and testing over a large data set obtained from real home measurements.

Data Mining and Privacy of Personal Behaviour Types in Smart Grid

Privacy protection is one of the key requirements of smart grids. To understand the importance of privacy threats it is necessary to study nature of power signals. In this paper, we propose a well-known statistical method which relies on the empirical probability distribution. The method is used to reveal trends in the power signal data and how these trends are changed if a) different data sampling rates are assumed, and b) a privacy algorithm is applied to protect the power data of different home appliances. Our results suggest that the privacy of personal behaviour types is exposed even if relatively infrequent measurements are obtained. On the other hand, battery-assisted home energy management solutions are more likely to protect the customers.

Information Theoretic Bounds for Compound MIMO Gaussian Channels

In this paper, achievable rates for compound Gaussian multiple-input-multiple-output (MIMO) channels are derived. Two types of channels, modeled in the frequency domain, are considered when: 1) the channel frequency response matrix H belongs to a subset of H infin normed linear space, and 2) the power spectral density (PSD) matrix of the Gaussian noise belongs to a subset of L 1 space. The achievable rates of these two compound channels are related to the maximin of the mutual information rate. The minimum is with respect to the set of all possible H matrices or all possible PSD matrices of the noise. The maximum is with respect to all possible PSD matrices of the transmitted signal with bounded power. For the compound channel modeled by the set of H matrices, it is shown, under certain conditions, that the code for the worst case channel can be used for the whole class of channels. For the same model, the water-filling argument implies that the larger the set of matrices H, the smaller the bandwidth of the transmitted signal will be. For the second compound channel, the explicit relation between the maximizing PSD matrix of the transmitted signal and the minimizing PSD matrix of the noise is found. Two PSD matrices are related through a Riccati equation, which is always present in Kalman filtering and liner-quadratic Gaussian control problems.

Robust Linear Channel Estimation Methods for Per-Subcarrier Transmit Antenna Selection

The linear minimum mean-squared error (MSE) channel estimator for systems employing per-subcarrier transmit antenna selection is developed. Frequency domain correlations after the selection process are shown to be approximated well using a simple function, which makes near-optimal channel estimation practically possible. However, the resultant estimators are not robust to errors in the assumed model, in terms of the antenna-to-subcarrier assignment used at transmission, as we motivate both analytically and via simulations. This is an issue when the channels for conveying this information to the receiver are severely constrained. We present windowed channel estimations and new robust estimation algorithms, either individually or in combination, as solutions to counter this sensitivity. The robust estimators are developed based on the principles of 1) minimising the worst case MSE over the set of possible models and 2) minimising the expected MSE over the set of possible models. The latter estimator is preferred due to the lower implementation complexity and better MSE performance. We also prove theoretical asymptotic (in signal-to-noise ratio) performances of the two estimators used with the proposed correlation model. Simulation results illustrate the performance gains and improved robustness offered by the developed estimators.

LDPC-Coded MIMO Optical Communication Over the Atmospheric Turbulence Channel

We describe a multiple optical sources - multiple detectors scheme, based on either repetition MIMO or space-time coding and low-density parity-check (LDPC) codes. The proposed scheme is able to operate under strong atmospheric turbulence and provides excellent coding gains. The LDPC codes are designed using the concept of pairwise-balanced designs. Bit-error rates and achievable information rates are reported assuming non-ideal photodetection. To improve the spectral efficiency we employ the concept of bit-interleaved LDPC-coded modulation based on pulse-amplitude modulation.

Real-time and low cost energy disaggregation of coarse meter data

A novel real-time non-intrusive appliance load monitoring algorithm is introduced. By applying finite state machine training, dynamic appliance disaggregation, rule-based filtering and discrete Fourier analysis of coarse data, several improvements are achieved: 1) reduced appliance model training complexity as compared to existing algorithms, and 2) novel and augmented detection stage. The application of this algorithm on real world data demonstrates that efficiency does not have to be compromised by the relatively lower complexity.