Look-Up Table based FHE System for Privacy Preserving Anomaly Detection in Smart Grids

In advanced metering infrastructure (AMI), the customers\u27 power consumption data is considered private but needs to be revealed to data-driven attack detection frameworks. In this paper, we present a system for privacy-preserving anomaly-based data falsification attack detection over fully homomorphic encrypted (FHE) data, which enables computations required for the attack detection over encrypted individual customer smart meter\u27s data. Specifically, we propose a homomorphic look-up table (LUT) based FHE approach that supports privacy preserving anomaly detection between the utility, customer, and multiple partied providing security services. In the LUTs, the data pairs of input and output values for each function required by the anomaly detection framework are stored to enable arbitrary arithmetic calculations over FHE. Furthermore, we adopt a private information retrieval (PIR) approach with FHE to enable approximate search with LUTs, which reduces the execution time of the attack detection service while protecting private information. Besides, we show that by adjusting the significant digits of inputs and outputs in our LUT, we can control the detection accuracy and execution time of the attack detection, even while using FHE. Our experiments confirmed that our proposed method is able to detect the injection of false power consumption in the range of 11-17 secs of execution time, depending on detection accuracy

Privacy-Preserving Data Falsification Detection in Smart Grids using Elliptic Curve Cryptography and Homomorphic Encryption

In an advanced metering infrastructure (AMI), the electric utility collects power consumption data from smart meters to improve energy optimization and provides detailed information on power consumption to electric utility customers. However, AMI is vulnerable to data falsification attacks, which organized adversaries can launch. Such attacks can be detected by analyzing customers\u27 fine-grained power consumption data; however, analyzing customers\u27 private data violates the customers\u27 privacy. Although homomorphic encryption-based schemes have been proposed to tackle the problem, the disadvantage is a long execution time. This paper proposes a new privacy-preserving data falsification detection scheme to shorten the execution time. We adopt elliptic curve cryptography (ECC) based on homomorphic encryption (HE) without revealing customer power consumption data. HE is a form of encryption that permits users to perform computations on the encrypted data without decryption. Through ECC, we can achieve light computation. Our experimental evaluation showed that our proposed scheme successfully achieved 18 times faster than the CKKS scheme, a common HE scheme

Simulations and predictions of mosquito populations in rural Africa using rainfall inputs from satellites and forecasts

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2010.Page 102 blank. Cataloged from PDF version of thesis.Includes bibliographical references (p. 94-101).This thesis describes studies on the use of the Hydrology, Entomology and Malaria Transmission Simulator (HYDREMATS) developed and tested against field data by Bomblies et al. (2008) in simulating and predicting the potential for malaria transmission in rural Africa. The first study examined the temporal resolution of rainfall input required by HYDREMATS. Simulations conducted over Banizoumbou village in Niger showed that for reasonably accurate simulation of mosquito populations, the model requires rainfall data with at least 1 hour resolution. The second study investigated whether HYDREMATS could be effectively forced by satellite based estimates of rainfall instead of ground based observations. The CPC Morphing technique (CMORPH) (Joyce et al., 2004) precipitation estimates distributed by NOAA are available at a 30-minute temporal resolution and 8 km spatial resolution. We compared mosquito populations simulated by HYDREMATS when the model is forced by adjusted CMORPH estimates and by ground observations. The results indicate that adjusted CMORPH rainfall estimates can be used with HYDREMATS to simulate the dynamics of mosquito populations and malaria transmission with accuracy similar to that obtained when using ground observations of rainfall. The third study tested the ability of HYDREMATS to make short term predictions about mosquito populations. A method was developed by which the rainfall forcing for HYDREMATS is constructed to suit a prediction mode. Observed rainfall is used up until the date of the prediction. The rainfall for the following two weeks (or four weeks) is assumed to be the seasonal mean for that period. HYDREMATS predictions using this method were not significantly different from simulations using observed data.This thesis describes studies on the use of the Hydrology, Entomology and Malaria Transmission Simulator (HYDREMATS) developed and tested against field data by Bomblies et al. (2008) in simulating and predicting the potential for malaria transmission in rural Africa. The first study examined the temporal resolution of rainfall input required by HYDREMATS. Simulations conducted over Banizoumbou village in Niger showed that for reasonably accurate simulation of mosquito populations, the model requires rainfall data with at least 1 hour resolution. The second study investigated whether HYDREMATS could be effectively forced by satellite based estimates of rainfall instead of ground based observations. The CPC Morphing technique (CMORPH) (Joyce et al., 2004) precipitation estimates distributed by NOAA are available at a 30-minute temporal resolution and 8 km spatial resolution. We compared mosquito populations simulated by HYDREMATS when the model is forced by adjusted CMORPH estimates and by ground observations. The results indicate that adjusted CMORPH rainfall estimates can be used with HYDREMATS to simulate the dynamics of mosquito populations and malaria transmission with accuracy similar to that obtained when using ground observations of rainfall. The third study tested the ability of HYDREMATS to make short term predictions about mosquito populations. A method was developed by which the rainfall forcing for HYDREMATS is constructed to suit a prediction mode. Observed rainfall is used up until the date of the prediction. The rainfall for the following two weeks (or four weeks) is assumed to be the seasonal mean for that period. HYDREMATS predictions using this method were not significantly different from simulations using observed data.by Teresa K. Yamana.S.M

Stochastic transitions of attractors in associative memory models with correlated noise

We investigate dynamics of recurrent neural networks with correlated noise to analyze the noise's effect. The mechanism of correlated firing has been analyzed in various models, but its functional roles have not been discussed in sufficient detail. Aoyagi and Aoki have shown that the state transition of a network is invoked by synchronous spikes. We introduce two types of noise to each neuron: thermal independent noise and correlated noise. Due to the effects of correlated noise, the correlation between neural inputs cannot be ignored, so the behavior of the network has sample dependence. We discuss two types of associative memory models: one with auto- and weak cross-correlation connections and one with hierarchically correlated patterns. The former is similar in structure to Aoyagi and Aoki's model. We show that stochastic transition can be presented by correlated rather than thermal noise. In the latter, we show stochastic transition from a memory state to a mixture state using correlated noise. To analyze the stochastic transitions, we derive a macroscopic dynamic description as a recurrence relation form of a probability density function when the correlated noise exists. Computer simulations agree with theoretical results.Comment: 21 page

Evaluation of mechanistic and statistical methods in forecasting influenza-like illness

A variety of mechanistic and statistical methods to forecast seasonal influenza have been proposed and are in use; however, the effects of various data issues and design choices (statistical versus mechanistic methods, for example) on the accuracy of these approaches have not been thoroughly assessed. Here, we compare the accuracy of three forecasting approaches-a mechanistic method, a weighted average of two statistical methods and a super-ensemble of eight statistical and mechanistic models-in predicting seven outbreak characteristics of seasonal influenza during the 2016-2017 season at the national and 10 regional levels in the USA. For each of these approaches, we report the effects of real time under- and over-reporting in surveillance systems, use of non-surveillance proxies of influenza activity and manual override of model predictions on forecast quality. Our results suggest that a meta-ensemble of statistical and mechanistic methods has better overall accuracy than the individual methods. Supplementing surveillance data with proxy estimates generally improves the quality of forecasts and transient reporting errors degrade the performance of all three approaches considerably. The improvement in quality from ad hoc and post-forecast changes suggests that domain experts continue to possess information that is not being sufficiently captured by current forecasting approaches

Early warnings of the potential for malaria transmission in rural Africa using the hydrology, entomology and malaria transmission simulator (HYDREMATS)

<p>Abstract</p> <p>Background</p> <p>Early warnings of malaria transmission allow health officials to better prepare for future epidemics. Monitoring rainfall is recognized as an important part of malaria early warning systems. The Hydrology, Entomology and Malaria Simulator (HYDREMATS) is a mechanistic model that relates rainfall to malaria transmission, and could be used to provide early warnings of malaria epidemics.</p> <p>Methods</p> <p>HYDREMATS is used to make predictions of mosquito populations and vectorial capacity for 2005, 2006, and 2007 in Banizoumbou village in western Niger. HYDREMATS is forced by observed rainfall, followed by a rainfall prediction based on the seasonal mean rainfall for a period two or four weeks into the future.</p> <p>Results</p> <p>Predictions made using this method provided reasonable estimates of mosquito populations and vectorial capacity, two to four weeks in advance. The predictions were significantly improved compared to those made when HYDREMATS was forced with seasonal mean rainfall alone.</p> <p>Conclusions</p> <p>HYDREMATS can be used to make reasonable predictions of mosquito populations and vectorial capacity, and provide early warnings of the potential for malaria epidemics in Africa.</p

Vibrational effect on the fragmentation dynamics of the C K-shell excited CF2CH2

Photoabsorption cross-sections of CF2CH2 were measured in the carbon K-edge region and linear time-of-flight mass spectra were acquired at some photon energies across the two π* peaks. The kinetic energy distributions of CH2+ and CF2+ with two components were deduced from the analysis of the mass spectra. The CH2+ ion with high kinetic energies increases with the extent of vibrational excitation of the CF 1s-1π* state, indicating that molecular vibrations play an important role in the photofragmentation of the inner-shell excited molecule