Multi-objective Configuration of a Secured Distributed Cloud Data Storage

García-Hernández, Luis Enrique; Tchernykh, Andrei; Miranda-López, Vanessa; Babenko, Mikhail; Avetisyan, Arutyun; Rivera-Rodríguez, Raúl; Радченко, Глеб; Barrios-Hernandez, Carlos Jaime; Castro, Harold; Drozdov, Alexander Yu.

doi:10.1007/978-3-030-41005-6_6

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…From (20) and (21), it follows that the secret sharing scheme has optimal redundancy (21). If the condition ≥…”

Section: Modification Of the Secret Sharing Schemementioning

confidence: 99%

“…Computational security for secret sharing schemes can be defined in more strong way [15]. It is based on the polynomial indistinguishability concept [16][17][18][19][20][21][22][23]. For any probability distribution ( , ), a secret sharing scheme is computationally secure if, for any pair of secrets (") , ($) and incomplete subsets of shares (") and ($) , the distributions N (") , (") V and N (") , (") Let = Pr N (") , (") V¡ = 1¡, = Pr N ($) , ($) V¡ = 1¡, = PrN N (") , (") V = 1V .…”

Section: T2x ›2$ X0)mentioning

confidence: 99%

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Computationally secure threshold secret sharing scheme with minimal redundancy

Babenko¹,

Tchernykh²,

Golimblevskaia³

et al. 2020

The International Workshop on Information, Computation, and Control Systems for Distributed Environments

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When designing and using distributed storage systems with cloud technology, the security issues become crucial. One of the promising mechanisms is the computationally secure threshold secret sharing scheme. We propose a computationally secure secret sharing scheme based on the minimally redundant modular code. It reduces the computational complexity of data encoding and decoding and reduce data redundancy. We show that it is computationally secure and provides data redundancy equivalent to the redundancy of the Rabin system. We demonstrate that the minimally redundant modular code does not satisfy the criterion of compactness of a sequence, but it can be used as an asymptotically ideal secret sharing scheme.

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“…From (20) and (21), it follows that the secret sharing scheme has optimal redundancy (21). If the condition ≥…”

Section: Modification Of the Secret Sharing Schemementioning

confidence: 99%

Section: T2x ›2$ X0)mentioning

confidence: 99%

Computationally secure threshold secret sharing scheme with minimal redundancy

Babenko¹,

Tchernykh²,

Golimblevskaia³

et al. 2020

The International Workshop on Information, Computation, and Control Systems for Distributed Environments

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“…Таким образом, современное развитие методов обучения ИНС является скорее интуитивноалгоритмическим, чем математическим. Применение стандартных алгоритмов облачных вычислений [9] хотя и позволяет упростить обучение моделей и эффективно использовать вычислительные ресурсы, не меняет принципиального подхода к обучению нейронных сетей. И, несмотря на достигнутые успехи и сокращение времени обучения в десятки, а иногда и в сотни раз, на данном направлении остается ряд задач, требующих теоретического осмысления.…”

Section: Introductionunclassified

Advanced supervised learning in multi-layer perceptrons to the recognition tasks based on correlation indicator

Вершков¹,

Babenko²,

Kuchukov³

et al. 2021

Proceedings of ISP RAS

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The article deals with the problem of recognition of handwritten digits using feedforward neural networks (perceptrons) using a correlation indicator. The proposed method is based on the mathematical model of the neural network as an oscillatory system similar to the information transmission system. The article uses theoretical developments of the authors to search for the global extremum of the error function in artificial neural networks. The handwritten digit image is considered as a one-dimensional input discrete signal representing a combination of "perfect digit writing" and noise, which describes the deviation of the input implementation from "perfect writing". The ideal observer criterion (Kotelnikov criterion), which is widely used in information transmission systems and describes the probability of correct recognition of the input signal, is used to form the loss function. In the article is carried out a comparative analysis of the convergence of learning and experimentally obtained sequences on the basis of the correlation indicator and widely used in the tasks of classification of the function CrossEntropyLoss with the use of the optimizer and without it. Based on the experiments carried out, it is concluded that the proposed correlation indicator has an advantage of 2-3 times.

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“…Обзор литературыВ этом разделе представлен краткий исследовательский обзор методов изучения и распознавания видов человеческой деятельности. В последнее время был проведен всесторонний анализ методов изучения видов деятельности наряду с методами моделирования сенсоров на основе машинного зрения и обучения, распознавания изображений и видео, а также мобильного распознавания активности с использованием сенсоров[32][33][34][35]. В работе[9] AR на основе данных комбинируется с методами на основе знаний для обработки неполных данных, поступающих от сенсоров.…”

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Machine Learning based Activity learning for Behavioral Contexts in Internet of Things

Сафьян¹,

Сарвар²,

Кайюм³

et al. 2021

Proceedings of ISP RAS

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Ontology based activity learning models play a vital role in diverse fields of Internet of Things (IoT) such as smart homes, smart hospitals or smart communities etc. The prevalent challenges with ontological models are their static nature and inability of self-evolution. The models cannot be completed at once and smart home inhabitants cannot be restricted to limit their activities. Also, inhabitants are not predictable in nature and may perform “Activities of Daily Life (ADL)” not listed in ontological model. This gives rise to the need of developing an integrated framework based on unified conceptual backbone (i.e. activity ontologies), addressing the lifecycle of activity recognition and producing behavioral models according to inhabitant’s routine. In this paper, an ontology evolution process has been proposed that learns particular activities from existing set of activities in daily life (ADL). It learns new activities that have not been identified by the recognition model, adds new properties with existing activities and learns inhabitant’s newest behavior of performing activities through Artificial Neural Network (ANN). The better degree of true positivity is evidence of activity recognition with detection of noisy sensor data. Effectiveness of proposed approach is evident from improved rate of activity learning, activity detection and ontology evolution.

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Multi-objective Configuration of a Secured Distributed Cloud Data Storage

Cited by 5 publications

References 15 publications

Computationally secure threshold secret sharing scheme with minimal redundancy

Computationally secure threshold secret sharing scheme with minimal redundancy

Advanced supervised learning in multi-layer perceptrons to the recognition tasks based on correlation indicator

Machine Learning based Activity learning for Behavioral Contexts in Internet of Things

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