Visible to the public Security Analysis of Key Extraction from Physical Measurements with Multiple Adversaries

TitleSecurity Analysis of Key Extraction from Physical Measurements with Multiple Adversaries
Publication TypeConference Paper
Year of Publication2018
AuthorsWang, X., Hou, Y., Huang, X., Li, D., Tao, X., Xu, J.
Conference Name2018 IEEE International Conference on Communications Workshops (ICC Workshops)
KeywordsAdversary Models, approximate conditional min- entropy, Channel models, Communication system security, computation complexity reduction, computational complexity, dynamic programming, dynamic programming approach, Entropy, exponential computation complexity, Heuristic algorithms, hidden Markov model, Hidden Markov models, HMM, Human Behavior, key extraction security analysis, legitimate wireless devices, Metrics, multiple adversaries algorithm, OME algorithm, physical measurements, private communication, pubcrawl, radio networks, Resiliency, Scalability, secret key extraction scheme, security, security evaluation metric, SOME algorithm, suboptimal method-with-multiple adversaries, telecommunication security, Viterbi algorithm, wireless channels, Wireless communication, wiretap channel model
AbstractIn this paper, security of secret key extraction scheme is evaluated for private communication between legitimate wireless devices. Multiple adversaries that distribute around these legitimate wireless devices eavesdrop on the data transmitted between them, and deduce the secret key. Conditional min-entropy given the view of those adversaries is utilized as security evaluation metric in this paper. Besides, the wiretap channel model and hidden Markov model (HMM) are regarded as the channel model and a dynamic programming approach is used to approximate conditional min- entropy. Two algorithms are proposed to mathematically calculate the conditional min- entropy by combining the Viterbi algorithm with the Forward algorithm. Optimal method with multiple adversaries (OME) algorithm is proposed firstly, which has superior performance but exponential computation complexity. To reduce this complexity, suboptimal method with multiple adversaries (SOME) algorithm is proposed, using performance degradation for the computation complexity reduction. In addition to the theoretical analysis, simulation results further show that the OME algorithm indeed has superior performance as well as the SOME algorithm has more efficient computation.
Citation Keywang_security_2018