Identification schemes in the post-quantum area based on multivariate polynomials with applications in cloud and IoT

2019 ◽  
pp. 181-207
Author(s):  
Sedat Akleylek ◽  
Meryem Soysaldı
Keyword(s):  
Multivariate Polynomials ◽  
Identification Schemes

scholarly journals A Novel Method for Polar Form of Any Degree of Multivariate Polynomials with Applications in IoT

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 903 ◽  
Author(s):  
Sedat Akleylek ◽  
Meryem Soysaldı ◽  
Djallel Boubiche ◽  
Homero Toral-Cruz
Keyword(s):  
Polynomial Systems ◽  
Multivariate Polynomials ◽  
Polar Form ◽  
Identification Schemes ◽  
Property Identification ◽  
Cubic Polynomial ◽  
Novel Method ◽  
Iot Devices ◽  
Polar Forms ◽  
Rfid Applications

Identification schemes based on multivariate polynomials have been receiving attraction in different areas due to the quantum secure property. Identification is one of the most important elements for the IoT to achieve communication between objects, gather and share information with each other. Thus, identification schemes which are post-quantum secure are significant for Internet-of-Things (IoT) devices. Various polar forms of multivariate quadratic and cubic polynomial systems have been proposed for these identification schemes. There is a need to define polar form for multivariate dth degree polynomials, where d ≥ 4 . In this paper, we propose a solution to this need by defining constructions for multivariate polynomials of degree d ≥ 4 . We give a generic framework to construct the identification scheme for IoT and RFID applications. In addition, we compare identification schemes and curve-based cryptoGPS which is currently used in RFID applications.

scholarly journals Choosing between identification schemes in noisy-news models

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Joshua C. C. Chan ◽  
Eric Eisenstat ◽  
Gary Koop
Keyword(s):  
Bayesian Methods ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Structural Shocks ◽  
Vector Autoregressive ◽  
Identification Scheme ◽  
Identification Schemes ◽  
The One ◽  
Small Models ◽  
Structural Vector

AbstractThis paper is about identifying structural shocks in noisy-news models using structural vector autoregressive moving average (SVARMA) models. We develop a new identification scheme and efficient Bayesian methods for estimating the resulting SVARMA. We discuss how our identification scheme differs from the one which is used in existing theoretical and empirical models. Our main contributions lie in the development of methods for choosing between identification schemes. We estimate specifications with up to 20 variables using US macroeconomic data. We find that our identification scheme is preferred by the data, particularly as the size of the system is increased and that noise shocks generally play a negligible role. However, small models may overstate the importance of noise shocks.

scholarly journals Factoring analytic multivariate polynomials and non-standard Cauchy–Riemann conditions

2014 ◽  
Vol 104 ◽  
pp. 43-57 ◽  
Author(s):  
Tomas Recio ◽  
J.-Rafael Sendra ◽  
Luis-Felipe Tabera ◽  
Carlos Villarino
Keyword(s):  
Multivariate Polynomials ◽  
Cauchy Riemann

scholarly journals Estimation of Norms of Multivariate Polynomials with Integral Coefficients

1999 ◽  
Vol 96 (2) ◽  
pp. 378-398
Author(s):  
Francisco Luquin ◽  
Concepción Besga
Keyword(s):  
Multivariate Polynomials
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