Pseudorandom Research Articles

FPGA based implementation of a perturbed Chen oscillator for secure embedded cryptosystems

This paper introduces an enhancement to the Chen chaotic system by incorporating a constant perturbation term d\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$d$$\\end{document} to one of the state variables, aiming to improve the performance of pseudo-random number generators (PRNGs). The perturbation significantly enhances the system's chaotic properties, resulting in superior randomness and increased security. An FPGA-based realization of a perturbed Chen oscillator (PCO)-derived PRNG is presented, tailored for embedded cryptosystems and implemented on a Nexys 4 FPGA card featuring the XILINX Artix-7 XC7A100T-1CSG324C integrated chip. The Xilinx-based system generator (XSG) tool is utilized to generate a digital version of the new oscillator, minimizing resource utilization. Experimental results demonstrate that the PCO-generated data successfully passes the NIST and TestU01 test suites. Additionally, statistical tests with key sensitivity are performed, validating the suitability of the designed PRNG for cryptographic applications. This establishes the PCO as a straightforward and efficient tool for multimedia security.

A 5D super-extreme-multistability hyperchaotic map based on parallel-cascaded memristors

A new 5-dimensional discrete memristors-based hyperchaotic map (5DMHM) is constructed by introducing four charge-controlled discrete memristors (DMs, coupled through parallel-cascade mixing operation) into the sine map. This 5DMHM has 4-dimensional space fixed points and its stability distributions are partitioned in coupling strength, memristor initial condition, and parameter planes. The dynamics of the map are investigated by employing 2D dynamical behavior and the first Lyapunov exponent (LE) distribution. The super extreme multistability is discovered through the basin of attraction, the initial-value-dependent LE spectra, bifurcation diagrams, the mean value of state variables, and trajectory plots. Moreover, a high level of complexity can be observed by calculating the spectral entropy complexity for various parameter planes. Subsequently, a microcontroller-based digital circuit is designed to implement the 5DMHM. The security analysis of the pseudorandom number generator (PRNG) designed by the 5DMHM is presented and the PRNG passes the NIST SP800-22 test. The comparison of the 5DMHM with the recent maps indicates a higher performance in secure communication and image encryption.

An optimized watermarking scheme based on genetic algorithm and elliptic curve

Digital watermarking serves as a crucial tool for tracing copyright infringements and ensuring the authenticity and integrity of sensitive information. The fundamental concept involves embedding a watermark in the host information, ensuring its undetectability by unauthorized parties. The efficacy of a watermarking scheme mainly depends on achieving high levels of imperceptibility, robustness, and embedding capacity. These attributes are intricately linked to both the selection of the host information segment and the embedding factor. Existing schemes often (i) employ the entire host information for embedding, incurring computational expenses, and (ii) optimize the embedding factor without considering imperceptibility, robustness, and embedding capacity simultaneously, resulting in less secure watermarks. To address these limitations, we introduce a novel watermarking scheme leveraging elliptic curves (ECs) and genetic algorithms (GA). We model the choice of the embedding part by generating pseudo-random numbers over ECs, taking advantage of their proven sensitivity, security, and low computational complexity. Due to parallel search and adaptability to non-linear relationships of GA, the scheme employs genetic optimization with a multivariate objective function to establish a balance between imperceptibility, robustness, and embedding capacity for optimal watermarked generation. Rigorous analysis and comparisons demonstrate that our proposed scheme attains significantly higher imperceptibility, robustness, and embedding capacity compared to existing optimized schemes. Furthermore, our scheme exhibits a speed advantage, being up to 278 and 21 times faster than optimized and non-optimized schemes, respectively, thereby affirming its practical applicability.

An Improved Multi-Chaotic Public Key Algorithm Based on Chebyshev Polynomials

Due to the similar characteristics of chaotic systems and cryptography, public key encryption algorithms based on chaotic systems are worth in-depth research and have high value for the future. Chebyshev polynomials have good properties and are often used in the design of public key algorithms. This paper improves the Bose Multi-Chaotic Public Key Cryptographic Algorithm (BMPKC) by applying Chebyshev polynomials. The proposed algorithm (CMPKC-) introduces the selective coefficient based on the properties of Chebyshev polynomials, allowing the special functions that need to be negotiated in the original system to be freely and randomly chosen as Chebyshev polynomials, and can also be expanded to m levels. The improved cryptographic algorithm also utilizes chaotic hash functions and logistic mapping to generate pseudo-random sequences and overcomes shortcomings of the Bose algorithm by iteratively iterating the selected Chebyshev polynomials based on the number of 0s or 1s in the pseudo-random sequence, thus providing better security. Analysis and software testing results indicate that this algorithm has strong robustness against brute force attacks, achieving a higher attack time for breaking the private key compared to the CEPKC, BMPKC, and CMPKC algorithms. Compared to the CMPKC algorithm, our proposal algorithm achieves better performance in the encryption and decryption phases. Furthermore, we combine this Multi-Chaotic System Key Exchange Protocol with the Advanced Encryption Standard (AES) algorithm, while providing a demonstration, offering more possibilities for practical applications of this system.

Encryption algorithm based on improved four-dimensional chaotic system and dynamic DNA encoding

An image encryption algorithm is proposed based on the combination of genetic (DNA) random coding and chaotic mapping. An image encryption algorithm based on an improved new four-dimensional chaotic system and DNA coding is proposed to address the problem that a single coding method is prone to selecting plaintext attacks. Based on a four-dimensional chaotic system existing in the literature, a new four-dimensional hyperchaotic system is obtained through improvement. The initial value of the system is generated based on SHA-256, zigzag transform. The input key and four pseudo-random chaotic sequences are generated iteratively. DNA chunking encoding, arithmetic operation, and decoding are implemented for the image disrupted based on the zigzag transform. The two-dimensional matrix constituted based on the Chaotic Sequence of Chebyshev to obtain the scrambled and diffused ciphertext image. Simulation experiments and security performance analysis show that the algorithm enhances the correlation between the key and the plaintext, the randomness of the encryption process, and effectively improves the anti-attack capability [H. Chen, J. Zhao et al., Appl. Res. Comput. 10, 0434 (2021)]. In this paper, 512 × 512 × 3 peppers color images are used for testing, and the correlation coefficients of adjacent pixels of the encrypted images are all close to 0, and the information entropy is all more significant than 7.997, which is relative to the theoretical value of 8. The experimental results show that the proposed algorithm improves the security of the ciphertext, increases the critical space, and, at the same time, resists various attack methods.

Image encryption algorithm based on multiple chaotic systems and improved Joseph block scrambling

Abstract With the rapid development of digital information technology, images are increasingly used in various fields. To ensure the security of image data, prevent unauthorized tampering and leakage, maintain personal privacy, and protect intellectual property rights, this study proposes an innovative color image encryption algorithm. Initially, the Mersenne Twister algorithm is utilized to generate high-quality pseudo-random numbers, establishing a robust basis for subsequent operations. Subsequently, two distinct chaotic systems, the autonomous non-Hamiltonian chaotic system and the tent-logistic-cosine chaotic mapping, are employed to produce chaotic random sequences. These chaotic sequences are used to control the encoding and decoding process of the DNA, effectively scrambling the image pixels. Furthermore, the complexity of the encryption process is enhanced through improved Joseph block scrambling. Thorough experimental verification, research, and analysis, the average value of the information entropy test data reaches as high as 7.999. Additionally, the average value of the number of pixels change rate (NPCR) test data is 99.6101%, which closely approaches the ideal value of 99.6094%. This algorithm not only guarantees image quality but also substantially raises the difficulty of decryption.

Lightweight Pseudo Random Number Generator for Embedded Systems

Lightweight Pseudo Random Number Generator for Embedded Systems

Characteristics of 3D coupled map lattice and its application in pseudo-random number generator

Characteristics of 3D coupled map lattice and its application in pseudo-random number generator

A Hindmarsh–Rose neuron model with electromagnetic radiation control for the mechanical optimization design

This paper proposes a Hindmarsh–Rose(HR) neuron model controlled by electromagnetic radiation to solve the mechanical optimization design problem. A novel memristor for simulating electromagnetic radiation is established to control the dynamical behavior of HR neuron models. By analyzing the HR model and using the numerical analysis methods of phase portrait, bifurcation diagram, Lyapunov exponent and spectral entropy (SE) complexity, the HR model is shown to have a hidden attractor and rich chaotic dynamics. Then, a chaotic optimization algorithm based on HR neuron model is designed to solve the mechanical optimization design of tension/compression coil springs and pressure vessels. The chaotic sequence of this algorithm which has high-quality pseudo-random number, has stronger global search ability. Simulation of two mechanical optimization design problems in comparison with some well-known optimization algorithms demonstrates the superiority of the HR in the solution quality.

Face privacy protection scheme by security-enhanced encryption structure and nonlinear dynamics

This article proposes a secure communication enhancement scheme based on face detection and chaotic partition permutation. The scheme uses edge detection technology to detect facial information, which is then used as an encryption object. The hash value of the plaintext image is extracted as the secret key to the chaotic sequence generated by the chaotic system. Then a series of encryption operations are performed on the face image to obtain the final ciphertext image. In this article, two chaotic systems are used to generate pseudo-random chaotic sequences for different encryption steps. The initial key is computed by combining the hash function of the image and external parameters. The experimental results and security analysis show that the algorithm has excellent encryption effectiveness and security performance against various typical attacks.

An Innovative Algorithm Based on Chaotic Maps Amalgamated with Bit-Level Permutations for Robust S-Box Construction and Its Application in Medical Image Privacy

Data security and privacy have become essential due to the increasingly advanced interconnectivity in today’s world, hence the reliance on cryptography. This paper introduces a new algorithm that uses a novel hybrid Tent–May chaotic map to generate pseudo-random numbers, as well as block encryption. We design a robust S-box by combining the Tent and May Maps, which yields a chaotic system with improved cryptographic properties. This S-box is a critical cryptographic primitive that significantly improves encryption security and leverages the strengths of both maps. The encryption process involves two key steps: block-wise substitution and permutation. First, we divide the image into 16×16 blocks, then substitute each pixel with the 8−byte key and S-box. Next, we convert the encrypted image back into vector form, reorganize it using the permutation vector based on the subgroups of S16, and finally return it to its original form. This approach greatly improves block cipher security when used, especially to protect medical images by guaranteeing their confidentiality and noninterference. Performance measures like PSNR, UACI, MSE, NCC, AD, SC, MD, and NAE prove how immune our method is to various cryptographic and statistical attacks, making it more accurate and more secure than the existing techniques.

An improved performance of reversible data hiding in encrypted images using decision tree algorithm

The advancements in increase of internet technology, transfer of data through insecure channel has become more popular. Now a day's most of the customers were turned to digital world and the channel is the main resource of communication. There is no guarantee that stored data will not be accessed by any hacker or cloud service provider. Privacy and Security is the main aspect of the rapid development in the digital storage. These conditions demand to make sure that the data is encrypted, stored and transmitted. Reversible Data Hiding in Encrypted images using machine learning is an emerging methodology used in the domain to secure the information for end-to-end secure data transmission. In this proposed method, all the data's can be Encrypted using Digital Keys, and these data can be embedded in cover images using decision tree algorithm. In the receiver side, the data can be retrieved back if receiver have right keys which is used while encryption. This system uses pseudo random number generation for generating multiple keys to encrypt and decrypt the secret data. This method is designed for encryption the Images and Text data. To increase further security and embedding capacity a machine learning algorithm is also implemented. The proposed Decision Tree method is tested on the standard image processing images and certain parameters are calculated for performance metrics and compare the results with existing schemes. The Algorithm is implemented in MATLAB and used the Statistics and Machine Learning library in MATLAB.

Pseudo-random Number Generator Influences on Average Treatment Effect Estimates Obtained with Machine Learning.

The use of machine learning to estimate exposure effects introduces a dependence between the results of an empirical study and the value of the seed used to fix the pseudo-random number generator. We used data from 10,038 pregnant women and a 10% subsample (N = 1004) to examine the extent to which the risk difference for the relation between fruit and vegetable consumption and preeclampsia risk changes under different seed values. We fit an augmented inverse probability weighted estimator with two Super Learner algorithms: a simple algorithm including random forests and single-layer neural networks and a more complex algorithm with a mix of tree-based, regression-based, penalized, and simple algorithms. We evaluated the distributions of risk differences, standard errors, and P values that result from 5000 different seed value selections. Our findings suggest important variability in the risk difference estimates, as well as an important effect of the stacking algorithm used. The interquartile range width of the risk differences in the full sample with the simple algorithm was 13 per 1000. However, all other interquartile ranges were roughly an order of magnitude lower. The medians of the distributions of risk differences differed according to the sample size and the algorithm used. Our findings add another dimension of concern regarding the potential for "p-hacking," and further warrant the need to move away from simplistic evidentiary thresholds in empirical research. When empirical results depend on pseudo-random number generator seed values, caution is warranted in interpreting these results.

(Invited) There’s More to a Probabilistic Neuromorphic Computing System Than Noisy Devices

A range of computing problems require understanding uncertainty, such as climate modeling, or use statistics to model problems that are otherwise difficult to solve, such as high energy particle collisions. Currently, these computations are handled by hardware where significant energy is spent to suppress stochasticity in materials and devices, and then significant computational resources are expended to re-introduce stochasticisty in algorithms. Instead, we take inspiration from the brain, which features 1015 stochastic synapses. This talk focuses on understanding how to leverage fluctuations in devices to do efficient sampling, which is a fundamental operation in many statistical approaches to computation [1]. In the first part of this talk, we use bitstreams generated by magnetic tunnel junction and tunnel diode devices to generate samples from different distributions, an elementary operation in statistical approaches to modeling. We show how to use elementary operations on multiple bits to improve both the accuracy and complexity of sampling. While intuition motivates the asking which of the two devices is more efficient at generating a random bitstream, in the second part of this talk, we show that this consideration is a small contribution to the overall circuit required to do a complete calculation. To significantly accelerate applications requires devices that minimize the energy and area cost of the CMOS parts of the design, as the true challenge lies in holistic codesign [2].At the heart of statistical approaches to computation is sampling. Typically, a uniform random sample is generated using a pseudo-random number generator (PRNG), followed by a mathematical operation to sample an application-relevant distribution. This sample is then plugged into a sequence of deterministic calculations that comprise a model. We use magnetic tunnel junctions and tunnel diodes to generate a fair coinflip, having equal probability of each of two outscomes, from which we create a uniform random sample. We show how to relate the quality of the device bitstreams to the quality of random samples [3]. While these devices consume significantly less energy than the PRNG, the energy consumed by the PRNG is a fraction of the energy consumed by a complete calculation. We next focus on moving the entire process of sampling non-uniform distributions into hardware. We show that weighted coinflips can be used to sample any distribution with a well-defined probability distribution function using a tree. Simple logic operations can be used to combine many inaccurate fair physical coinflips to produce a single high-accuracy weighted logical coinflip, and high quality samples from non-uniform distributions. Overall, a simple implementation uses a few hundred physical coinflips, some simple logic operations (shift register, comparison, XOR extractor), and memory access to produce a sample. This success points to the potential for moving more of the model into increasingly sophisticated sampling schemes.Having established how the basic element of the computation are connected, we are now ready to examine its efficiency compared to a PRNG, whose cost is roughly nJ/operation. Thus far, we have ignored the analog signal transduction attached to the coinflip devices and the logic operations that tie them together. Coinflip devices which cannot be directly integrated with logic will suffer from a von Neumann bottleneck. Thus, the coinflip devices need to be intimately integrated with logic, requiring paying a per-device transduction penalty. We find the energy cost for even the simplest signal transduction – a stimulating pulse and a latching output – is in the 100 fJ – 1 pJ per device range, which is larger than the energy consumed by either magnetic tunnel junctions or tunnel diodes. Meanwhile, the most expensive component of the logic operations stem from the fine-grained integration of memory. While only 32 weighted coinflips are needed to draw a 32-bit non-uniform sample, there are ~232 possible weights needed. We discuss schemes to prune the values needed to avoid the situation where the weights occupy all of a 1 cm2 die. In sum, the energy and area cost of the coinflip device are less important than the energy cost of signal transduction, and the area taken by memory for storing weights of a sampling tree.We emphasize that none of our estimates represents a tight optimization of any part of an imagined probabilistic computer. Still, in a full-system implementation, the efficient management of signal transduction and memory clearly outweight the energy or space consumed by the coinflip devices themselves. These factors comprise the key variables for stochastic devices that need to be accounted for in holistic codesign for probabilistic computing.SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.

Design of a discrete memristive chaotic map: fractional-order memory, dynamics and application

In this paper, a discrete fracmemristor (DFM) model is derived based on the Caputo difference, and a new fractional-order chaotic map is designed. Dynamics of the proposed map is investigated in detail by means of Lyapunov exponent spectra, bifurcation diagrams, PE complexity and multistability analyses. Compared with the coupled discrete integer-order memristor (DIM), the map coupled with the DFM products richer dynamics, including larger attractor distribution, fewer numerically periodic windows, and higher complexity. Besides, the order becomes additional bifurcation parameter. Finally, the proposed map is implemented on Field-Programmable Gate Array (FPGA) platform, and applied in a pseudorandom number generator (PRNG), which further demonstrates its application value.

Research on high-precision encoder based on optical-magnetic combination structure to measure absolute angle

As a kind of sensor for real-time feedback and dynamic monitoring of angular velocity and angular displacement of the system, the common photoelectric encoder is widely used in many fields such as new energy electric vehicles, servo closed-loop control systems, industrial control, security monitoring. In order to improve the resolution and accuracy, a kind of optical and magnetic combined encoder which combines photoelectricity and multi-pole magnetoelectricity is designed in this paper, where the light-blocking disk of photoelectric sensor is designed according to the pseudo-random sequence, and the photoelectric coding is used to determine the magnetic interval of multi-pole. The absolute angle is calculated according to the number of multiple circles plus the angle of single circle. The experimental results at different temperatures (−40 °C∼100 °C) show that the accuracy of the proposed combined encoder can reach ±0.08°, which is improved by 0.37° compared with that of the single-pole encoder. Finally, the combined encoder was applied to the pan-tilt-zoom (PTZ) camera of a video surveillance enterprise, and the experiment showed that its repeated positioning accuracy reached ±0.02°, which is greatly improved compared with single-pole encoder.

A High-Performance FPGA PRNG Based on Multiple Deep-Dynamic Transformations.

Pseudo-random number generators (PRNGs) are important cornerstones of many fields, such as statistical analysis and cryptography, and the need for PRNGs for information security (in fields such as blockchain, big data, and artificial intelligence) is becoming increasingly prominent, resulting in a steadily growing demand for high-speed, high-quality random number generators. To meet this demand, the multiple deep-dynamic transformation (MDDT) algorithm is innovatively developed. This algorithm is incorporated into the skewed tent map, endowing it with more complex dynamical properties. The improved one-dimensional discrete chaotic mapping method is effectively realized on a field-programmable gate array (FPGA), specifically the Xilinx xc7k325tffg900-2 model. The proposed pseudo-random number generator (PRNG) successfully passes all evaluations of the National Institute of Standards and Technology (NIST) SP800-22, diehard, and TestU01 test suites. Additional experimental results show that the PRNG, possessing high novelty performance, operates efficiently at a clock frequency of 150 MHz, achieving a maximum throughput of 14.4 Gbps. This performance not only surpasses that of most related studies but also makes it exceptionally suitable for embedded applications.

Reconfigurable Image Confusion Scheme Using Large Period Pseudorandom Bit Generator Based on Coupled-Variable Input LCG and Clock Divider

This paper presents a reconfigurable image confusion scheme, which uses Linear Congruential Generators (LCGs)-based Pseudorandom Bits Generator (PRBG). The PRBG is based on the variable input-coupled LCG with a reconfigurable clock divider. The proposed algorithm encrypts the input image up to four times successively using different random sequences in every attempt. This new scheme aims to efficiently extract statistically strong pseudorandom sequences from a proposed PRBG with a large keyspace and simultaneously increase the security level of the encrypted image. This PRBG was initially designed on Virtex-5 (XC5VLX110T), Virtex-7 (XC7VX330T) and Artix-7 (XC7A100T) Field Programmable Gate Arrays (FPGAs). The statistical properties of the proposed PRBG for four different configurations are verified by the National Institute of Standards and Technology (NIST) tests. Thereafter, a reconfigurable encryption/decryption algorithm that uses the proposed PRBG is developed for secure image encryption. The encryption process was accomplished using the MATLAB tool after obtaining the PRBG keys from the FPGA. To show the quality and strength of the encryption process, security analysis [correlations and Number of Pixels Change Rate (NPCR)] is performed. Security analysis results are compared with the conventional encryption algorithm to show that the developed reconfigurable encryption scheme provides better results in security tests.

Ultra High multi channel clock frequency(THz, PHz, EHz, ZHz,YHz,XHz,WHz) baud rate speed PRBS Transceiver HDL RTL ASIC Design for Ultra High long Distance Wireless Communication Engineering Smart Computing Products/ Applications

The Key Objective is HDL RTL Design Architecture of Ultra high multi Clock Frequency Speed Rate ( MHz, GHz, THz, PHz, EHz, ZHz, etc) Bits Per Second Baud Rate ) P.R.B.S(Pseudo Random Binary Sequence) Transceiver Soft A.S.I.C IP Core product for identification of the property of Different Pseudo Random Binary Sequence Patterns (Seed Words) of 2e 7 -1, 2e10 -1, 2e 15 -1, 2e 23 -1, 2e 31 -1 tapped elements as per C.C.I.T.T-I.T.U Standards and IEEE-754 Single and Double Data Rate Data Precision Standards (32 bit & 64 Bit Data Width ) suited for Very Advanced Futuristic Hi-tech Smart High Speed Long Distance Wireless Digital Communication A.S.I.C Products / Applications like Space/Satellite, Aerospace and Large Data Processing and computing like High Speed Internet and Cloud Computing based Hi-Fi Industrial Data Automation Standard Ultra High Speed Wireless Communication A.S.I.C products and Applications-3G,4G,5G etc. The Multi channel PRBS Transceiver consists Transmitter and receiver of different PRBS patterns- 2e7 -1, 2e10 -1, 2e15 -1, 2e23 -1, 2e31 -1 NRZ. The data input transmitted and received serially in the form bit by bit. These different pattern sequences are Designated as per CCITT ITU 0.151/O.152/O.153 & AT&T Standards. The Aim and purpose of invention of Ultra high multichannel Clock frequency PRBS Transceiver is for transmit and receive data serially by using Tx_in and Tx_out, rxin, rxout signals and The Transceiver is processed the low frequency signal input with different high speed carrier wave frequencies in the form of different PRBS pseudo random binary sequence seed word bit/byte patterns -2e7 -1, 2e10 -1, 2e15 -1, 2e23 -1, 2e31 -1 NRZ and also on receiver side processed with the above patterns. Materials and Methods: Transmission and reception of Data serially based on the Deterministic random seed word pattern methods of different PRBS 2e7 -1, 2e10 -1, 2e15 -1, 2e23 -1, 2e31 -1 tapped sequence Elements and All these PRBS are purely synchronized with Ultra high clock frequency(MHz, GHz, THz, PHz, EHz, ZHz, YHz, XHz, WHz). The Soft IP Core Designed by System Verilog HDL/ Verilog HDL. RTL Design Simulation done by Synopsys VCS 2020.1 software and Altera Model-Sim Software and Logic Design Flow & Synthesis done by Xilinx ISE and Altera Quartus EDA Tools. Results: Generation of Simulation Display and waveform for Ultra High Clock frequency(MHz, GHz, THz, PHz, EHz, ZHz, YHz, XHz, WHz) Synchronized Pseudo Random Binary Sequence (PRBS) Register Transfer Level (RTL) Generators, Transceiver for efficient and effective High Quality Data transmission and Reception of Various tapped sequence patterns for Identification of property of different PRBS patterns2e7 -1, 2e10-1, 2e15-1, 2e23 -1, 2 31 -1 NRZ of Ultra high speed Long distance wireless engineering Applications/ products.

Designing five-dimensional non-degeneracy chaotic system and its application in reversible data hiding

The application of privacy protection techniques combining chaotic systems with data hiding has attracted considerable attention in recent years. However, the existing schemes have been found to be defective in terms of security and embedding capacity. This paper proposes a construction method for a 5-dimensional non-degeneracy chaotic system (5D-NDCS) to address the shortcomings of the previous approach. The 5D-NDCS possesses five positive Lyapunov exponents, and each dimension can generate chaotic sequences with strong randomness. Utilizing the pseudo-random sequence generated by the 5D-NDCS to encrypt the compressed image can enhance the security of the encrypted image. Subsequently, this paper introduces a joint bit-plane compression (JBPC) algorithm, which is based on the chaotic system and pixel value prediction technique. This algorithm effectively utilizes the pixel correlation of the original image to compress the bit sequence sufficiently, thereby reserving the hiding space. Finally, this paper proposes a reversible data hiding in encrypted images (RDHEI) scheme that supports multiple data hiders, based on the JBPC algorithm. The experimental results demonstrate the scheme’s advantages in terms of embedding rate, security, and fault tolerance.