A substitution box(S-Box)is a crucial component of contemporary cryptosystems that provide data protection in block *** the moment,chaotic maps are being created and extensively used to generate these SBoxes as a chao...
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A substitution box(S-Box)is a crucial component of contemporary cryptosystems that provide data protection in block *** the moment,chaotic maps are being created and extensively used to generate these SBoxes as a chaotic map assists in providing disorder and resistance to combat cryptanalytical *** this paper,the construction of a dynamic S-Box using a cipher key is proposed using a novel chaotic map and an innovative tweaking *** projected chaotic map and the proposed tweak approach are presented for the first time and the use of parameters in their workingmakes both of these dynamic in *** tweak approach employs cubic polynomials while permuting the values of an initial S-Box to enhance its cryptographic *** of the parameters are provided using the cipher key and a small variation in values of these parameters results in a completely different unique *** analysis and exploration confirmed that the projected chaoticmap exhibits a significant amount of chaotic *** security assessment in terms of bijectivity,nonlinearity,bits independence,strict avalanche,linear approximation probability,and differential probability criteria are utilized to critically investigate the effectiveness of the proposed S-Box against several *** proposed S-Box’s cryptographic performance is comparable to those of recently projected S-Boxes for its adaption in real-world security *** comparative scrutiny pacifies the genuine potential of the proposed S-Box in terms of its applicability for data security.
The experimental studies presented in this paper reveal that existing thermal management systems (TMS) and temperature-informed charging algorithms exhibit a response time lag of at least 5.3 minutes due to their reli...
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This paper presents a study on the effect of using a smaller number of inputs in the FPGA logic block calculated according to a pre-compiled model based on Rent's rule. This rule, when applied to the FPGA logic bl...
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We address a problem involving a buyer seeking to train a logistic regression model by acquiring data from privacy-sensitive sellers. Along with compensating the sellers for their data, the buyer provides differential...
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ISBN:
(数字)9798350316339
ISBN:
(纸本)9798350316346
We address a problem involving a buyer seeking to train a logistic regression model by acquiring data from privacy-sensitive sellers. Along with compensating the sellers for their data, the buyer provides differential privacy guarantees to them where the payments depend on the privacy guarantees. In addition, each seller has a different privacy sensitivity associated with their data, which is the cost per unit of loss of privacy. The buyer transacts sequentially with the sellers, wherein the seller will disclose their privacy sensitivity, and the buyer immediately provides a payment and guarantees differential privacy. After receiving the payment, the seller provides their data to the buyer. The buyer’s goal is to optimize a weighted combination of test loss and payments, i.e., achieve a tradeoff between getting a good ML model and limiting its payments. Additionally, the buyer must design the payments and differential privacy guarantees in an online fashion. Further, the online problem is historydependent, which adds to the challenge. Consequently, we design a payment mechanism that ensures incentive compatibility and individual rationality and is asymptotically optimal. Additionally, we also provide experimental results to validate our findings.
Low-capacity scenarios have become increasingly important in the technology of the Internet of Things (IoT) and the next generation of wireless networks. Such scenarios require efficient and reliable transmission over...
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Estimating high-dimensional covariance matrices is crucial in various domains. This work considers a scenario where two collaborating agents access disjoint dimensions of m samples from a high-dimensional random vecto...
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Estimating high-dimensional covariance matrices is crucial in various domains. This work considers a scenario where two collaborating agents access disjoint dimensions of m samples from a high-dimensional random vector, and they can only communicate a limited number of bits to a central server, which wants to accurately approximate the covariance matrix. We analyze the fundamental trade-off between communication cost, number of samples, and estimation accuracy. We prove a lower bound on the error achievable by any estimator, highlighting the impact of dimensions, number of samples, and communication budget. Furthermore, we present an algorithm that achieves this lower bound up to a logarithmic factor, demonstrating its near-optimality in practical settings. Copyright 2024 by the author(s)
Renewable energy generation sources (RESs) are gaining increased popularity due to global efforts to reduce carbon emissions and mitigate effects of climate change. Planning and managing increasing levels of RESs, spe...
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Energy management requires reliable tools to support decisions aimed at optimising consumption. Advances in data-driven models provide techniques like Non-Intrusive Load Monitoring (NILM), which estimates the energy d...
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Wearable devices have transformed from novelties into indispensable companions for millions, finding applications in health and wellness, empowering individuals to proactively manage their well-being. Among these, pul...
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In the typical multiagent formation tracking problem centered on consensus, the prevailing assumption in the literature is that the agents' nonlinear models can be approximated by integrator systems, by their feed...
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