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Nanoscale CMOS Memory-Based Security Primitive Design
Published in Mark Tehranipoor, Domenic Forte, Garrett S. Rose, Swarup Bhunia, Security Opportunities in Nano Devices and Emerging Technologies, 2017
Validating that a particular source is sufficiently random is a nontrivial task, especially when the randomness is extracted from memory cells that may be biased. When cells are purportedly unbiased and random, one can run a battery of statistical tests to check for different statistical biases. Yet, in the case of biased bits, one should estimate the entropy of the source. Min-entropy, when treating the initial state of an SRAM as a random variable X, describes the information learned from observing the most likely outcome of that variable (Equation 3.1). Min-entropy is an important measure because it bounds the success rate of an adversary that knows the exact distribution of initial states of the SRAM. When using a hash function or other deterministic extractor, the attacker will achieve the highest chance of success by always guessing that the input to the extractor is the most likely valuation of X. In simple terms, if a source has at least 128 bits of min-entropy, then no initial state occurs with a probability of more than 2−128, and an attacker that always guesses that the source produces its most likely value will not be correct in this guess with probability exceeding 2−128. To illustrate the amount of randomness that can be extracted from SRAM power-up state, one previous work calculates that 2k bytes [31] of SRAM are needed to generate a 256-bit random number, and another calculates that 4k bits are needed to generate a 128-bit random number [10]. H∞(X)=−log2(max(p(xi)))
DC optimization for constructing discrete Sugeno integrals and learning nonadditive measures
Published in Optimization, 2020
G. Beliakov, M. Gagolewski, S. James
Maximizing the Shannon entropy is suitable in the Choquet integral setting, resulting in equidistant values of μ (for subset cardinality greater than k), i.e. for some for , and larger subset measures are calculated as , . As an analogue of k-interactivity in the Sugeno integral setting here, we maximize the Rényi min-entropy , which results in solving the problem The optimal solution is for all .
Efficient chosen-ciphertext secure hybrid encryption scheme tolerating continuous leakage attacks
Published in Journal of the Chinese Institute of Engineers, 2019
Yanwei Zhou, Bo Yang, Yong Yu, Arshad Khan
Given and bits leakage, the average min-entropy of is at least . Thus, we can get . To sum up, if can break the security of our new construction with advantage , then, for any , can solve the hardness of DDH problem and break the target collision resistance of with advantages and such that .
How geopolitical risk drives exchange rate and oil prices? A wavelet-based analysis
Published in Energy Sources, Part B: Economics, Planning, and Policy, 2021
Wenqi Duan, Adnan Khurshid, Abdur Rauf, Khalid Khan, Adrian Cantemir Calin
The relationship between GPR and OPs has widely been tested in the empirical literature (Khan et al. 2020a; Su et al. 2020). Huang et al. (2021) examine the nonlinear dynamic correlation between GPR and OPs using the DCC-MVGARCH methods and notice a bidirectional nonlinear Granger causality between the variables. Su et al. (2020) examine the impact of GPR on OP and financial liquidity using Wavelet analysis for Saudi Arabia. The results show that GPR affects OPs and positively affects financial liquidity. Conversely, Abdel-Latif and El-Gamal (2020) tested a similar relationship using a Global Vector Autoregression (GVAR) model and concluded that GPR is not influencing oil prices. Khan et al. (2020a), using a Generalized Supremum Augmented Dickey-Fuller (GSADF) approach, tests the crude oil price bubble and finds out that political-economic stability reduces oil price volatility. Another interesting conclusion deriving from a regression analysis is that GPR and military conflicts can be caused by oil resources (Leder and Shapiro 2008). Khan, Su, and Tao (2020b) and Kollias, Papadamou, and Arvanitis (2013) checked the connection using a wavelet and GARCH (1,1) in-mean model and observed that geopolitical events and insurgency, respectively, raise oil prices. Cotet and Tsui (2013) used a panel data methodology to discover that GPRs upsurged when oil resources were abundant. In terms of strength and sign, Bazzi and Blattman (2014) show via a regression model that the variations of oil prices have little effect on GPR, and Chen et al. (2016), employing a Structural Vector Autoregression (SVAR) model, report that GPR has a strong positive effect on OP. Relying on panel data modeling, Caselli, Morelli, and Rohner (2015) conclude that many regional disputes will occur due to oil supplies affecting OPs. While analyzing shift inefficiency caused by major geopolitical intervention, Bariviera, Zunino, and Rosso (2016) argue that oil prices correlate with geopolitical events using the permutation min-entropy process. Via an indirect channel, Doukas, Flamos, and Psarras (2011) review the possible risk involved in oil and gas supply and affirm that any interruption in the oil supply chain can affect demand supply and eventually OPs. Similarly, according to Roupas, Flamos, and Psarras (2011), terrorist attacks and other risks make European Oil and Gas supply more vulnerable, which can cause possible interruptions and damage to the recipient economies.