An Integrated Dual Entropy Core True Random Number Generator

Cicek, Ihsan; Pusane, Alí Emre; Dündar, Günhan

doi:10.1109/tcsii.2016.2568181

Cited by 29 publications

(10 citation statements)

References 15 publications

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“…Field programmable gate array-based applications provide high-speed implementation and reconfigurable design process. In cryptographic applications, FPGAs occupy an important place [28][29][30][31][32][33][34][35]. The proposed design in the third scenario is run on Virtex II Pro XC2VP30 FPGA development board by using Verilog HDL as the source type.…”

Section: Field Programmable Gate Array Implementation Of Chaotic Cellmentioning

confidence: 99%

Chaotic cellular neural network‐based true random number generator

Karakaya

Çelik

Gülten

2017

Circuit Theory & Apps

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Summary This paper presents implementation of a chaotic cellular neural network (CNN)‐based true random number generator on a field programmable gate array (FPGA) board. In this implementation, discrete time model of the chaotic CNN is used as the entropy source. Random number series are generated for three scenarios. Obtained number series are tested by using NIST 800.22 statistical test suite. Also, the scale index technique is carried out for these three scenarios to determine the degree of non‐periodicity for key stream. Copyright © 2017 John Wiley & Sons, Ltd.

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Section: Field Programmable Gate Array Implementation Of Chaotic Cellmentioning

confidence: 99%

Chaotic cellular neural network‐based true random number generator

Karakaya

Çelik

Gülten

2017

Circuit Theory & Apps

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“…Nevertheless, the statistical characteristics of the sequence generated by these systems are highly sensitive to the chaotic system parameters perturbations, causing an issue that must be carefully taken into account when designing the hardware implementation of these TRNGs [96,97,103,[122][123][124][125]. In Figure 12, a CMOS circuit to implement the algebraic calculation of the Sawtooth nonlinear function shown in Figure 11(a) is reported, using cascode current mirrors [111]. In Figure 12, a CMOS circuit to implement the algebraic calculation of the Sawtooth nonlinear function shown in Figure 11(a) is reported, using cascode current mirrors [111].…”

Section: Chaotic Circuitsmentioning

confidence: 99%

“…The electronic design of piecewise linear chaotic maps has been investigated following different approaches and targeting different applications, including true random numbers generation, secure communication, and colored noise generation [96, 104-111, 120, 121]. The complete iterated execution of the computation I n+1 = f (I n ) is obtained by means of a delay block realized with track-and-hold switchedcurrent stages [111,126]. The circuit calculates f (I n ), being the chaotic state variable represented by a current.…”

Section: Chaotic Circuitsmentioning

confidence: 99%

“…In Figure 12, a CMOS circuit to implement the algebraic calculation of the Sawtooth nonlinear function shown in Figure 11(a) is reported, using cascode current mirrors [111]. The circuit calculates f (I n ), being the chaotic state variable represented by a current.…”

Section: Chaotic Circuitsmentioning

confidence: 99%

“…The circuit calculates f (I n ), being the chaotic state variable represented by a current. The complete iterated execution of the computation I n+1 = f (I n ) is obtained by means of a delay block realized with track-and-hold switchedcurrent stages [111,126].…”

Section: Chaotic Circuitsmentioning

confidence: 99%

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Embedded electronic circuits for cryptography, hardware security and true random number generation: an overview

Acosta

Addabbo

Tena-Sánchez

2016

Circuit Theory & Apps

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We provide an overview of selected crypto-hardware devices, with a special reference to the lightweight electronic implementation of encryption/decryption schemes, hash functions, and true random number generators. In detail, we discuss the hardware implementation of the chief algorithms used in private-key cryptography, public-key cryptography, and hash functions, discussing some important security issues in electronic crypto-devices, related to side-channel attacks (SCAs), fault injection attacks, and the corresponding design countermeasures that can be taken. Finally, we present an overview about the hardware implementation of true random number generators, discussing the chief electronic sources of randomness and the types of post-processing techniques used to improve the statistical characteristics of the generated random sequences.A. J. ACOSTA, T. ADDABBO AND E. TENA-SÁNCHEZ resource consumption. This matter sets the point for the lightweight cryptography, that is, the subfield of cryptography aiming to provide solutions tailored for resource-constrained devices.According to Elsevier Scopus, the largest database of research peer-reviewed literature, since 2010, about 40k documents are returned if searching the keyword 'cryptography' [6]. A huge subset of these papers deals with conceptual, algorithmic, software, hardware solutions that may be taken into account in lightweight cryptography. In the face of such a vast literature, in this work, we provide a brief overview of selected crypto-hardware devices, with a special reference to the lightweight electronic implementation of encryption/decryption schemes, hash functions, and true random number generators (TRNGs).This paper is organized as in the following. In Section 2, we introduce some terminology and present an overview of the hardware implementation of the chief algorithms used in private-key cryptography, public-key cryptography (PKC), and hash functions. In Sections 3 and 4, we introduce some important security concerns about electronic crypto-devices, discussing SCAs, fault injection attacks, and the corresponding countermeasures that can be taken in the hardware design. Finally, Sections 5-8 are devised to provide an overview about the electronic implementation of TRNGs. In detail, in Sections 5 and 6, we discuss about security flaws, statistical defects, and predictability of TRNGs, presenting the chief sources of randomness used nowadays for their hardware implementation. In Sections 7 and 8, we discuss an overview of the different post-processing techniques aimed to improve the statistical characteristics of the generated random sequences and the evaluation methods used to assess the reliability of cryptographic TRNGs. Conclusion and References close the paper. CRYPTOGRAPHIC ALGORITHMSCryptographic algorithms aim to convert secret data into an unreadable code for non authorized persons, protecting secret information from theft or alteration and also enabling authentication. For better understanding, in the next sections, we define the following...

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IEFHAC: Image encryption framework based on hessenberg transform and chaotic theory for smart health

Jan

Parah

Malik

2022

Multimed Tools Appl

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Smart cities aim to improve the quality of life by utilizing technological advancements. One of the main areas of innovation includes the design, implementation, and management of data-intensive medical systems also known as big-data Smart Healthcare systems. Smart health systems need to be supported by highly efficient and resilient security frameworks. One of the important aspects that smart health systems need to provide, is timely access to high-resolution medical images, that form about 80% of the medical data. These images contain sensitive information about the patient and as such need to be secured completely. To prevent unauthorized access to medical images, the process of image encryption has become an imperative task for researchers all over the world. Chaos-based encryption has paved the way for the protection of sensitive data from being altered, modified, or hacked. In this paper, we present an Image Encryption Framework based on Hessenberg transform and Chaotic encryption (IEFHAC), for improving security and reducing computational time while encrypting patient data. IEFHAC uses two 1D-chaotic maps: Logistic map and Sine map for the confusion of data, while diffusion has been achieved by applying the Hessenberg household transform. The Sin and Logistic maps are used to regeneratively affect each other’s output, as such dynamically changing the key parameters. The experimental analysis demonstrates that IEFHAC shows better results like NPCR ranging from 99.66 to 100%, UACI of 37.39%, lesser computational time of 0.36 s, and is more robust to statistical attacks.

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An Integrated Dual Entropy Core True Random Number Generator

Cited by 29 publications

References 15 publications

Chaotic cellular neural network‐based true random number generator

Chaotic cellular neural network‐based true random number generator

Embedded electronic circuits for cryptography, hardware security and true random number generation: an overview

IEFHAC: Image encryption framework based on hessenberg transform and chaotic theory for smart health

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