scholarly journals A New Geometric Data Perturbation Method for Data Anonymization Based on Random Number Generators

2021 ◽  
Author(s):  
Merve Kanmaz ◽  
Muhammed Ali Aydın ◽  
Ahmet Sertbaş
Keyword(s):  
Classification Accuracy ◽  
Random Number ◽  
Rapid Development ◽  
Random Noise ◽  
Personal Data ◽  
Noise Model ◽  
Data Perturbation ◽  
Random Number Generators ◽  
Data Anonymization ◽  
Geometric Data

With the technology’s rapid development and its involvement in all areas of our lives, the volume and value of data have become a significant field of study. Valuation of the data to this extent has produced some consequences in terms of people’s knowledge. Data anonymization is the most important of these issues in terms of the security of personal data. Much work has been done in this area and continues to being done. In this study, we proposed a method called RSUGP for the anonymization of sensitive attributes. A new noise model based on random number generators has been proposed instead of the Gaussian noise or random noise methods, which are being used conventionally in geometric data perturbation. We tested our proposed RSUGP method with six different databases and four different classification methods for classification accuracy and attack resistance; then, we presented the results section. Experiments show that the proposed method was more successful than the other two classification accuracy, attack resistance, and runtime.

scholarly journals A Survey on True Random Number Generators Based on Chaos

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Fei Yu ◽  
Lixiang Li ◽  
Qiang Tang ◽  
Shuo Cai ◽  
Yun Song ◽  
...  
Keyword(s):  
Chaotic System ◽  
Key Management ◽  
Random Number ◽  
Initial Conditions ◽  
Rapid Development ◽  
Current Mode ◽  
Random Numbers ◽  
Random Number Generators ◽  
Network Information ◽  
Survey Paper

With the rapid development of communication technology and the popularization of network, information security has been highly valued by all walks of life. Random numbers are used in many cryptographic protocols, key management, identity authentication, image encryption, and so on. True random numbers (TRNs) have better randomness and unpredictability in encryption and key than pseudorandom numbers (PRNs). Chaos has good features of sensitive dependence on initial conditions, randomness, periodicity, and reproduction. These demands coincide with the rise of TRNs generating approaches in chaos field. This survey paper intends to provide a systematic review of true random number generators (TRNGs) based on chaos. Firstly, the two kinds of popular chaotic systems for generating TRNs based on chaos, including continuous time chaotic system and discrete time chaotic system are introduced. The main approaches and challenges are exposed to help researchers decide which are the ones that best suit their needs and goals. Then, existing methods are reviewed, highlighting their contributions and their significance in the field. We also devote a part of the paper to review TRNGs based on current-mode chaos for this problem. Finally, quantitative results are given for the described methods in which they were evaluated, following up with a discussion of the results. At last, we point out a set of promising future works and draw our own conclusions about the state of the art of TRNGs based on chaos.

Memory efficient lagged-Fibonacci random number generators for GPU supercomputing

2015 ◽  
Vol 21 (2) ◽  
Author(s):  
Timothy D. Andersen ◽  
Michael Mascagni
Keyword(s):  
Graphics Processing Units ◽  
Random Number ◽  
Gpu Computing ◽  
Rapid Development ◽  
Rapid Expansion ◽  
Random Number Generators ◽  
Statistical Quality ◽  
Large State Space ◽  
Open Standard ◽  
Memory Efficient

AbstractGraphics Processing Units (GPUs) bring the promise of supercomputing power for a fraction of the cost of traditional supercomputing, with possible speed-ups over comparable CPU hardware of one or two orders of magnitude. Rapid development of both proprietary libraries, such as NVIDIA's CUDA, and an open standard, OpenCL, have opened the doors to the GPU's cheap computing power. Unfortunately, random number generators (RNGs) have been slow to catch up with the rapid expansion of GPU computing. The number of types of RNGs available for GPUs is small, and the statistical quality of those provided with standard libraries are frequently unknown. Because specific RNGs may have statistical quality for certain applications, new kinds of RNGs must be made available for GPU computing to bring the full power of GPUs to different kinds of research. Lagged-Fibonacci Generators (LFGs), in particular, have been difficult to develop for memory-challenged GPUs because of their large state space, which is unfortunate because they have excellent statistical properties for many applications. In this paper, we discuss our implementation of memory efficient, integer, cycle-split, additive and multiplicative LFGs for both CUDA and OpenCL. The latter LFG has been implemented neither for GPUs nor as a split-steam parallel generator before. We also discuss portability and reproducibility between CPUs and GPUs.

Random Noise Based Perturbation Approach Using Pseudo Random Number Generators for Achieving Privacy in Data Mining

2015 ◽  
Vol 12 (12) ◽  
pp. 5463-5466
Author(s):  
G Manikandan ◽  
N Sairam ◽  
P Rajendiran ◽  
R Balakrishnan ◽  
N. Rajesh Kumar ◽  
...  
Keyword(s):  
Data Mining ◽  
Random Number ◽  
Random Noise ◽  
Perturbation Approach ◽  
Random Number Generators ◽  
Pseudo Random Number

scholarly journals Design And Analysis of Analog TRNG Using Sample and Hold Circuit

2018 ◽  
Vol 7 (3.8) ◽  
pp. 69 ◽  
Author(s):  
B Aksshaya ◽  
G Madhura L. V. ◽  
Nivethashri S ◽  
Vishnuvarthini T ◽  
Mohankumar N
Keyword(s):  
Random Number ◽  
Random Noise ◽  
Binary Sequence ◽  
Random Number Generation ◽  
Primary Input ◽  
Random Number Generators ◽  
Security Applications ◽  
Sample And Hold ◽  
Input Source ◽  
Almost All

Implementation of analog True random number generators is inevitable in almost all the security applications and encryption protocols nowadays. Although many digital True Random Number Generators are available, we proposed a method of random number generation using analog module of mixed signals. In actual fact generation of True Random Numbers is by utilizing the sample and hold circuit which is controlled by another random clock source, and a post processing circuit for generation of unpredictable binary sequence of numbers. The primary input source is an analog signal, essentially highly random noise from the external environment. The high unpredictability, less resource and simple circuit design are some highlights of the proposed work. Finally, the randomness is evaluated using NIST test suites and results are plotted and analyzed.  

scholarly journals TRNGs from Pre-Formed ReRAM Arrays

Cryptography ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 8
Author(s):  
Bertrand Cambou ◽  
Donald Telesca ◽  
Sareh Assiri ◽  
Michael Garrett ◽  
Saloni Jain ◽  
...  
Keyword(s):  
Random Number ◽  
Random Number Generator ◽  
Random Access ◽  
Random Number Generators ◽  
Cryptographic Algorithms ◽  
Cryptographic Keys ◽  
Low Levels ◽  
Stochastic Properties ◽  
Pseudo Random Number Generator ◽  
Number Of Cells

Schemes generating cryptographic keys from arrays of pre-formed Resistive Random Access (ReRAM) cells, called memristors, can also be used for the design of fast true random number generators (TRNG’s) of exceptional quality, while consuming low levels of electric power. Natural randomness is formed in the large stochastic cell-to-cell variations in resistance values at low injected currents in the pre-formed range. The proposed TRNG scheme can be designed with three interconnected blocks: (i) a pseudo-random number generator that acts as an extended output function to generate a stream of addresses pointing randomly at the array of ReRAM cells; (ii) a method to read the resistance values of these cells with a low injected current, and to convert the values into a stream of random bits; and, if needed, (iii) a method to further enhance the randomness of this stream such as mathematical, Boolean, and cryptographic algorithms. The natural stochastic properties of the ReRAM cells in the pre-forming range, at low currents, have been analyzed and demonstrated by measuring a statistically significant number of cells. Various implementations of the TRNGs with ReRAM arrays are presented in this paper.

scholarly journals Design of True Random Number Circuit with Controllable Frequency

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1517
Author(s):  
Xinsheng Wang ◽  
Xiyue Wang
Keyword(s):  
Random Number ◽  
Noise Source ◽  
State Of The Art ◽  
Building Blocks ◽  
Encryption Algorithm ◽  
Random Numbers ◽  
Random Number Generators ◽  
Random Telegraph Noise ◽  
Telegraph Noise ◽  
Novel Method

True random number generators (TRNGs) have been a research hotspot due to secure encryption algorithm requirements. Therefore, such circuits are necessary building blocks in state-of-the-art security controllers. In this paper, a TRNG based on random telegraph noise (RTN) with a controllable rate is proposed. A novel method of noise array circuits is presented, which consists of digital decoder circuits and RTN noise circuits. The frequency of generating random numbers is controlled by the speed of selecting different gating signals. The results of simulation show that the array circuits consist of 64 noise source circuits that can generate random numbers by a frequency from 1 kHz to 16 kHz.

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