Visible to the public Asymptotic Analysis of a New Low Complexity Encryption Approach for the Internet of Things, Smart Cities and Smart Grid

TitleAsymptotic Analysis of a New Low Complexity Encryption Approach for the Internet of Things, Smart Cities and Smart Grid
Publication TypeConference Paper
Year of Publication2017
AuthorsSamudrala, A. N., Blum, R. S.
Conference Name2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC)
Date Publishedjul
ISBN Number978-1-5386-0504-2
Keywordsasymptotic analysis, binary stochastic encryption approach, compositionality, cryptography, encrypted nonbinary quantized data, Encryption, encryption approaches, fixed threshold binary quantization limits parameter estimation, Human Behavior, human factors, Information security, Internet of Things, legitimate fusion center, LFC estimation, low complexity encryption, low complexity encryption approach, maximum likelihood estimation, maximum-likelihood estimation, mean square error methods, nonbinary generalized case, nonbinary quantized observations, optimal estimators, parameter estimation, party fusion center perspectives, probability, pubcrawl, quantisation (signal), quantizer, resilience, Resiliency, scalar parameters, secure estimation, Sensor networks, sensor outputs, Sensors, smart cities, Smart grid, Smart Grid Sensors, stochastic encryption, telecommunication security, TPFC estimation, vector parameter estimation, wireless medium, Wireless sensor networks, WSN

Parameter estimation in wireless sensor networks (WSN) using encrypted non-binary quantized data is studied. In a WSN, sensors transmit their observations to a fusion center through a wireless medium where the observations are susceptible to unauthorized eavesdropping. Encryption approaches for WSNs with fixed threshold binary quantization were previously explored. However, fixed threshold binary quantization limits parameter estimation to scalar parameters. In this paper, we propose a stochastic encryption approach for WSNs that can operate on non-binary quantized observations and has the capability for vector parameter estimation. We extend a binary stochastic encryption approach proposed previously, to a non-binary generalized case. Sensor outputs are quantized using a quantizer with R + 1 levels, where R $e$ 1, 2, 3,..., encrypted by flipping them with certain flipping probabilities, and then transmitted. Optimal estimators using maximum-likelihood estimation are derived for both a legitimate fusion center (LFC) and a third party fusion center (TPFC) perspectives. We assume the TPFC is unaware of the encryption. Asymptotic analysis of the estimators is performed by deriving the Cramer-Rao lower bound for LFC estimation, and the asymptotic bias and variance for TPFC estimation. Numerical results validating the asymptotic analysis are presented.

Citation Keysamudrala_asymptotic_2017