Classification of pseudo-random number generators applied to information security

Chugunkov, Ilya V.; Ivanov, Michael A.; Gridneva, E. A.; Shestakova, Natalia Yu.

doi:10.1109/eiconrus.2017.7910569

Cited by 11 publications

(7 citation statements)

References 5 publications

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“…means that it is possible to solve discrete logarithm in a group of elliptic curve points, i.e., an inverted function (1) or (2).…”

Section: Dual Elliptic Curve Deterministic Random Bit Generatormentioning

confidence: 99%

“…where 0 r is a value that can be obtained by expression (2) when substituting as an argument to the function of scalar multiplication of points of a number 0 s Seed = .…”

Section: Dual Elliptic Curve Deterministic Random Bit Generatormentioning

confidence: 99%

“…ANDOM and pseudo-random number generators are important and extremely powerful cryptographic primitives [1][2][3]. They are used independently (as a source of primary entropy, key generators, initialization vectors and other special data) or as part of the other mechanisms of cryptographic protection (encryption, cryptographic hashing, zero-knowledge proof protocols, etc.)…”

Section: Introductionmentioning

confidence: 99%

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Elliptic Curve Pseudorandom Bit Generator with Maximum Period Sequences

Кuznetsov¹,

Gorbenko²,

Kiian³

et al. 2021

IJC

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Pseudo-random number generator is an important mechanism for cryptographic information protection. It can be used independently to generate special data or as the most important element of security of other mechanisms for cryptographic information protection. The application of transformations in a group of points of elliptic and hypereliptic curves is an important direction for the designing of cryptographically stable pseudo-random sequences generators. This approach allows us to build the resistant cryptographic algorithms in which the problem of finding a private key is associated with solving the discrete logarithm problem. This paper proposes a method for generating pseudo-random sequences of the maximum period using transformations on the elliptic curves. The maximum sequence period is provided by the use of recurrent transformations with the sequential formation of the elements of the point group of the elliptic curve. In this case, the problem of finding a private key is reduced to solving a theoretically complex discrete logarithm problem. The article also describes the block diagram of the device for generating pseudo-random sequences and the scheme for generating internal states of the generator.

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“…means that it is possible to solve discrete logarithm in a group of elliptic curve points, i.e., an inverted function (1) or (2).…”

Section: Dual Elliptic Curve Deterministic Random Bit Generatormentioning

confidence: 99%

“…where 0 r is a value that can be obtained by expression (2) when substituting as an argument to the function of scalar multiplication of points of a number 0 s Seed = .…”

Section: Dual Elliptic Curve Deterministic Random Bit Generatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Elliptic Curve Pseudorandom Bit Generator with Maximum Period Sequences

Кuznetsov¹,

Gorbenko²,

Kiian³

et al. 2021

IJC

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show abstract

“…In order to guarantee the voter anonymity, Mix net technique should be used [10]. One important point while using public key cryptography and RSA, the choosing must be made for safe and secure algorithms to be used in random numbers generator [11] [12].…”

Section: Cryptography Public Keymentioning

confidence: 99%

Transparent E-Voting dApp Based on Waves Blockchain and RIDE Language

Faour

2019

2019 XVI International Symposium "Problems of Redundancy in Information and Control Systems" (REDUNDANCY)

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“…In recent years, random numbers (RNs) have been widely used in the fields of encryption algorithm, wireless communication, statistical analysis, and radio frequency identification (RFID) [1][2][3][4][5][6][7][8][9][10]. RNs can be divided into pseudorandom numbers (PRNs) [11] and true random numbers (TRNs) [12].…”

Section: Introductionmentioning

confidence: 99%

A Novel TRNG Based on Traditional ADC Nonlinear Effect and Chaotic Map for IoT Security and Anticollision

Sun

et al. 2021

Security and Communication Networks

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In the rapidly developing Internet of Things (IoT) applications, how to achieve rapid identification of massive devices and secure the communication of wireless data based on low cost and low power consumption is the key problem to be solved urgently. This paper proposes a novel true random number generator (TRNG) based on ADC nonlinear effect and chaotic map, which can be implemented by traditional processors with built-in ADCs, such as MCU, DSP, ARM, and FPGA. The processor controls the ADC to sample the changing input signal to obtain the digital signal DADC and then extracts some bits of DADC to generate the true random number (TRN). At the same time, after a delay based on DADC, the next time ADC sampling is carried out, and the cycle continues until the processor stops generating the TRN. Due to the nonlinear effect of ADC, the DADC obtained from each sampling is stochastic, and the changing input signal will sharply change the delay time, thus changing the sampling interval (called random interval sampling). As the input signal changes, DADC with strong randomness is obtained. The whole operation of the TRNG resembles a chaotic map, and this method also eliminates the pseudorandom property of chaotic map by combining the variable input signal (including noise) with the nonlinear effect of ADC. The simulation and actual test data are verified by NIST, and the verification results show that the random numbers generated by the proposed method have strong randomness and can be used to implement TRNG. The proposed TRNG has the advantages of low cost, low power consumption, and strong compatibility, and the rate of generating true random number is more than 1.6 Mbps (determined by ADC sampling rate and processor frequency), which is very suitable for IoT sensor devices for security encryption algorithms and anticollision.

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Classification of pseudo-random number generators applied to information security

Cited by 11 publications

References 5 publications

Elliptic Curve Pseudorandom Bit Generator with Maximum Period Sequences

Elliptic Curve Pseudorandom Bit Generator with Maximum Period Sequences

Transparent E-Voting dApp Based on Waves Blockchain and RIDE Language

A Novel TRNG Based on Traditional ADC Nonlinear Effect and Chaotic Map for IoT Security and Anticollision

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