Performance evaluation of secret sharing schemes with data recovery in secured and reliable heterogeneous multi-cloud storage

Tchernykh, Andrei; Miranda-López, Vanessa; Babenko, Mikhail; Armenta-Cano, Fermin A.; Радченко, Глеб; Drozdov, Alexander Yu.; Avetisyan, Arutyun

doi:10.1007/s10586-018-02896-9

Cited by 24 publications

(11 citation statements)

References 37 publications

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“…This method, instead of an approximate conversion (a conversion with the excess flow), estimates and uses the exact value for the conversion [19,[21][22][23][24][25]. Equation (17) can be reformulated to find as follows:…”

Section: Extension Methods Using Floating-point Arithmeticmentioning

confidence: 99%

Neural network method for base extension in residue number system

Babenko¹,

Shiriaev²,

Tchernykh³

et al. 2020

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

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Confidential data security is associated with the cryptographic primitives, asymmetric encryption, elliptic curve cryptography, homomorphic encryption, cryptographic pseudorandom sequence generators based on an elliptic curve, etc. For their efficient implementation is often used Residue Number System that allows executing additions and multiplications on parallel computing channels without bit carrying between channels. A critical operation in Residue Number System implementations of asymmetric cryptosystems is base extension. It refers to the computing a residue in the extended moduli without the application of the traditional Chinese Remainder Theorem algorithm. In this work, we propose a new way to perform base extensions using a Neural Network of a final ring. We show that it reduces 11.7% of the computational cost, compared with state-of-the-art approaches.

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Section: Extension Methods Using Floating-point Arithmeticmentioning

confidence: 99%

Neural network method for base extension in residue number system

Babenko¹,

Shiriaev²,

Tchernykh³

et al. 2020

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

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“…The advantages of this representation include inherently carry-free operations, smaller numbers that encode original numbers, and no-positional system with independent arithmetic units. The use of RNS has gained considerable interest in HE schemes due to some characteristics of the system: security, parallel processing, scalability, error detection, and correction [16,17].…”

Section: Residue Number Systemmentioning

confidence: 99%

Privacy-Preserving Logistic Regression as a Cloud Service Based on Residue Number System

Cortés-Mendoza

Tchernykh

Babenko

et al. 2020

Communications in Computer and Information Science

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The security of data storage, transmission, and processing is emerging as an important consideration in many data analytics techniques and technologies. For instance, in machine learning, the datasets could contain sensitive information that cannot be protected by traditional encryption approaches. Homomorphic encryption schemes and secure multi-party computation are considered as a solution for privacy protection. In this paper, we propose a homomorphic Logistic Regression based on Residue Number System (LR-RNS) that provides security, parallel processing, scalability, error detection, and correction. We verify it using six known datasets from medicine (diabetes, cancer, drugs, etc.) and genomics. We provide experimental analysis with 30 configurations for each dataset to compare the performance and quality of our solution with the state of the art algorithms. For a fair comparison, we use the same 5-fold cross-validation technique. The results show that LR-RNS demonstrates similar accuracy and performance of the classification algorithm at various thresholds settings but with the reduction of training time from 85.4% to 97.5%.

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“…For this problem, the next solutions are applied. The first one is to unbalance RNS moduli when one or more RNS moduli are several times larger than the rest of the moduli [19]. The disadvantage of this approach is related to the case when errors occurred in the largest RNS modulus so that the amount of incorrect data is significant and may exceed the threshold.…”

Section: B Error Correction Codementioning

confidence: 99%

2Lbp-RRNS: Two-Levels RRNS With Backpropagation for Increased Reliability and Privacy-Preserving of Secure Multi-Clouds Data Storage

et al. 2020

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Cloud storage as service is the mainstream technology used to retain digital data. However, there are significant risks for confidentiality, integrity, and availability violation associated with the loss of information, denial of access, technical failures, etc. In this article, we propose a two-level 2Lbp-RRNS scheme based on a Redundant Residue Number System with a backpropagation and hamming distance mechanisms for increasing reliability of a configurable and secure multi-cloud data storage. We provide a theoretical analysis of the 2Lbp-RRNS solution as an extension of the classical 2L-RRNS and a variant of fully homomorphic encryption for privacy-preserving, parallel processing, and scalability. We formulate, explain, and prove its main properties to extend existing knowledge within the limits of the critical bounding RRNS assumptions. We show that 2Lbp-RRNS can identify and recover more errors than traditional 2L-RRNS. We provide the upper bounds of the traditional threshold 2L-RRNS and our solution to estimate the number of detectable and correctable errors. We study various data access scenarios and show that it detects 1.58x and corrects 3.37x more errors than 2L-RRNS, on average. We also provide efficient implementations of encoding and decoding algorithms MRC8, and MRC16 based on the Mixed-Radix system, Finite Ring Neuronal Network, and signed binary window method. We evaluate encoding/decoding speeds using three algorithms: Mignotte, MRC8, and MRC16. The experimental system includes seven cloud storages: DropBox, GoogleDrive, OneDrive, Sharefile, Box, Egnyte, and Salesforce. To assess the efficiency of the system on real data, we vary scenarios of the first and second levels. The results show that our solution outperforms MRC8 by 2.53x (1.78x), and Mignotte by 4.83x (11.43x) for the encoding (decoding) speed, respectively.

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Performance evaluation of secret sharing schemes with data recovery in secured and reliable heterogeneous multi-cloud storage

Cited by 24 publications

References 37 publications

Neural network method for base extension in residue number system

Neural network method for base extension in residue number system

Privacy-Preserving Logistic Regression as a Cloud Service Based on Residue Number System

2Lbp-RRNS: Two-Levels RRNS With Backpropagation for Increased Reliability and Privacy-Preserving of Secure Multi-Clouds Data Storage

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