Biblio

Information privacy and security has become a necessity in the rapid growth of computer technology. A new algorithm for image encryption is proposed in this paper; using hash functions, chaotic map and two levels of diffusion process. The initialization key for chaos map is generated with the help of two hash functions. The initial seed for these hash functions is the sum of rows, columns and pixels across the diagonal of the plain image. Firstly, the image is scrambled using quantization unit. In the first level of diffusion process, the pixel values of the scrambled image are XOR with the normalized chaotic map. Odd pixel value is XOR with an even bit of chaotic map and even pixel is XOR with an odd bit of chaotic map. To achieve strong encryption, the image undergoes a second level of diffusion process where it is XOR with the map a finite number of times. After every round, the pixel array is circular shifted three times to achieve a strong encrypted image. The experimental and comparative analysis done with state of the art techniques on the proposed image encryption algorithm shows that it is strong enough to resist statistical and differential attacks present in the communication channel.

Secure hash functions are widely used in cryptographic algorithms to secure against diverse attacks. A one-way secure hash function is used in the various research fields to secure, for instance, blockchain. Notably, most of the hash functions provide security based on static parameters and publicly known operations. Consequently, it becomes easier to attack by the attackers because all parameters and operations are predefined. The publicly known parameters and predefined operations make the oracle regenerate the key even though it is a one-way secure hash function. Moreover, the sensitive data is mixed with the predefined constant where an oracle may find a way to discover the key. To address the above issues, we propose a novel one-way secure hash algorithm, OSHA for short, to protect sensitive data against attackers. OSHA depends on a pseudo-random number generator to generate a hash value. Particularly, OSHA mixes multiple pseudo-random numbers to produce a secure hash value. Furthermore, OSHA uses dynamic parameters, which is difficult for adversaries to guess. Unlike conventional secure hash algorithms, OSHA does not depend on fixed constants. It replaces the fixed constant with the pseudo-random numbers. Also, the input message is not mixed with the pseudo-random numbers; hence, there is no way to recover and reverse the process for the adversaries.

Impactful data breaches that exposed the online accounts and financial information of billions of individuals have increased recently because of the digitization of numerous industries. As a result, the need for comprehensive cybersecurity measures has risen, particularly with regard to the safekeeping of user passwords. Strong password storage security ensures that even if an attacker has access to compromised data, they are unable to utilize the passwords in attack vectors like credential-stuffing assaults. Additionally, it will reduce the risk of threats like fraudulent account charges or account takeovers for users. This study compares the performance of several hashing algorithms, including Bcrypt, SHA-256 and MD5 and how bcrypt algorithm outperforms the other algorithms. Reversal of each of the results will be attempted using Rainbow Tables for better understanding of hash reversals and the comparisons are tabulated. The paper provides a detail implementation of bcrypt algorithm and sheds light on the methodology of BCRYPT hashing algorithm results in robust password security. While SHA-256 hashing algorithms are, easily susceptible to simple attacks such as brute force as it a fast algorithm and making bcrypt more favorable.

With the rapid popularity of the network, the development of information encryption technology has a significant role and significance in securing network security. The security of information has become an issue of concern to the whole society, and the study of cryptography has been increasingly concerned, and the hash function is the core of modern cryptography, the most common hash algorithms are MD5 series of algorithms, SHA series of algorithms. MD5 is a popular and excellent typical Hash encryption technology today, which is used for password management, electronic signature, spam screening. In this paper, we focus on the improved MD5 algorithm with more efficiency, focusing on the internal structure of MD5, and finally making it more efficient in retrieval.

The use of digital images is increasingly widespread currently. There is a need for security in digital photos. Cryptography is a technique that can be applied to secure data. In addition to safety, data integrity also needs to be considered to anticipate the image being manipulated. The hash function is a technique that can be used to determine data authentication. In this study, the Rail Fence Cipher and Myszkowski algorithms were used for the encryption and decryption of digital images, as the Secure Hash Algorithm (SHA-256) algorithm. Rail Fence Cipher Algorithm is a transposition algorithm that is quite simple but still vulnerable. It is combined with the Myszkowski Algorithm, which has a high level of complexity with a simple key. Secure Hash Algorithm (SHA-256) is a hash function that accepts an input limit of fewer than 2∧64 bits and produces a fixed hash value of 256 bits. The tested images vary based on image resolution and can be encrypted and decrypted well, with an average MSE value of 4171.16 and an average PSNR value of 11.96 dB. The hash value created is also unique. Keywords—Cryptography, Hash Function, Rail Fence Cipher, Myszkowski, SHA-256, Digital image.

Hash-based data structures and algorithms are currently flourishing on the Internet. It is an effective way to store large amounts of information, especially for applications related to measurement, monitoring and security. At present, there are many hash table algorithms such as: Cuckoo Hash, Peacock Hash, Double Hash, Link Hash and D-left Hash algorithm. However, there are still some problems in these hash table algorithms, such as excessive memory space, long insertion and query operations, and insertion failures caused by infinite loops that require rehashing. This paper improves the kick-out mechanism of the Cuckoo Hash algorithm, and proposes a new hash table structure- Odd-Even Hash (OE Hash) algorithm. The experimental results show that OE Hash algorithm is more efficient than the existing Link Hash algorithm, Linear Hash algorithm, Cuckoo Hash algorithm, etc. OE Hash algorithm takes into account the performance of both query time and insertion time while occupying the least space, and there is no insertion failure that leads to rehashing, which is suitable for massive data storage.

A hash is a fixed-length output of some data that has been through a one-way function that cannot be reversed, called the hashing algorithm. Hashing algorithms are used to store secure information, such as passwords. They are stored as hashes after they have been through a hashing algorithm. Also, hashing algorithms are used to insure the checksum of certain data over the internet. This paper discusses how Ibn Omar's hashing algorithm will provide higher security for data than other hash functions used nowadays. Ibn Omar's hashing algorithm in produces an output of 1024 bits, four times as SHA256 and twice as SHA512. Ibn Omar's hashing algorithm reduces the vulnerability of a hash collision due to its size. Also, it would require enormous computational power to find a collision. There are eight salts per input. This hashing algorithm aims to provide high privacy and security for users.

As an emerging technology, IoT is rapidly revolutionizing the global communication network with billions of new devices deployed and connected with each other. Many of these devices collect and transfer a large amount of sensitive or mission critical data, making security a top priority. Compared to traditional Internet, IoT networks often operate in open and harsh environment, and may experience frequent delays, traffic loss and attacks; Meanwhile, IoT devices are often severally constrained in computational power, storage space, network bandwidth, and power supply, which prevent them from deploying traditional security schemes. Authentication is an important security mechanism that can be used to identify devices or users. Due to resource constrains of IoT networks, it is highly desirable for the authentication scheme to be lightweight while also being highly effective. In this paper, we developed and evaluated a hash-chain-based multi-node mutual authentication algorithm. Nodes on a network all share a common secret key and broadcast to other nodes in range. Each node may also add to the hash chain and rebroadcast, which will be used to authenticate all nodes in the network. This algorithm has a linear running time and complexity of O(n), a significant improvement from the O(nˆ2) running time and complexity of the traditional pairwise multi-node mutual authentication.

To verify the integrity and confidentiality of data communicated through the web is a very big issue worldwide because every person wants very fast computing and secure electronic data communication via the web. The authentication of electronic data is done by hashing algorithms. Presently researchers are using one-time padding to convert variable-length input messages into a block of fixed length and also using constant initial values that are constant for any input message. So this reason we are proposing the autonomous initial value proposed secure hash algorithm-256 (AIVPSHA256) and we are enhancing the performance of the hash function by designing and compuiting its experimental results in python 3.9.5 programming language.

Passwords are generally used to keep unauthorized users out of the system. Password hacking has become more common as the number of internet users has extended, causing a slew of issues. These problems include stealing the confidential information of a company or a country by adversaries which harm the economy or the security of the organization. Hackers often use password hacking for criminal activities. It is indispensable to protect passwords from hackers. There are many hacking methods such as credential stuffing, social engineering, traffic interception, and password spraying for hacking the passwords. So, in order to control hacking, there are hashing algorithms that are mostly used to hash passwords making password cracking more difficult. In this proposed work, different hashing algorithms such as SHA-1, MD-5, Salted MD-5, SHA-256, and SHA-512 have been used. And the MySQL database is used to store the hash values of passwords that are generated using various hash functions. It is proven that SHA is better than MD-5 and Salted MD-5. Whereas in the SHA family, SHA-512 and SHA-256 have their own benefits. Four new hashing functions have been proposed using the combination of existing algorithms like SHA-256, and SHA-512 namely SHA-256\_with\_SHA-256, SHA-256\_ With\_SHA-512,SHA-512\_With\_SHA-512,and SHA-512\_ With\_SHA-256. They provide strong hash value for passwords by which the security of passwords increases, and hacking can be controlled to an extent.

A secure hash code or message authentication code is a one-way hash algorithm. It is producing a fixed-size hash function to be used to check verification, the integrity of electronic data, password storage. Numerous researchers have proposed hashing algorithms. They have a very high time complexity based on several steps, initial value, and key constants which are publically known. We are focusing here on the many exiting algorithms that are dependent on the initial value and key constant usage to increasing the security strength of the hash function which is publically known. Therefore, we are proposing autonomous initial value proposed secure hash algorithm (AIVPSHA64) in this research paper to produce sixty-four-bit secure hash code without the need of initial value and key constant, it is very useful for a smart card to verify their identity which has limited memory space. Then evaluate the performance of hash function using autonomous initial value proposed secure hash algorithm (AIVPSHA64) and will compare the result, which is found by python-3.9.0 programming language.

The cloud storage system is a very big boon for the organizations and individuals who are all in the need of storage space to accommodate huge volume of digital data. The cloud storage space can handle various types of digital data like text, image, video and audio. Since the storage space can be shared among different users, it is possible to have duplicate copies of data in the storage space. An efficient mechanism is required to identify the digital data uniquely in order to check the duplicity. There are various ways by which the digital data can be identified. One among such technique is hash-based identification. Using cryptographic hashing algorithms, every data can be uniquely identified. The unique property of hashing algorithm helps to identify the data uniquely. In this research work, we are going to discuss the advantage of using cryptographic hashing algorithm for digital data identification and the comparison of various hashing algorithms.

Recently, with the increasing number of large-scale remote sensing images, the demand for large-scale remote sensing image object classification is growing and attracting the interest of many researchers. Hashing, because of its low memory requirements and high time efficiency, has widely solve the problem of large-scale remote sensing image. Supervised hashing methods mainly leverage the label information of remote sensing image to learn hash function, however, the similarity of the original feature space cannot be well preserved, which can not meet the accurate requirements for object classification of remote sensing image. To solve the mentioned problem, we propose a novel method named Optimized Projection Supervised Discrete Hashing(OPSDH), which jointly learns a discrete binary codes generation and optimized projection constraint model. It uses an effective optimized projection method to further constraint the supervised hash learning and generated hash codes preserve the similarity based on the data label while retaining the similarity of the original feature space. The experimental results show that OPSDH reaches improved performance compared with the existing hash learning methods and demonstrate that the proposed method is more efficient for operational applications.

Hash constructions are used in cryptographic algorithms from very long. Features of Hashes that gives the applications the confidence to use them in security methodologies is “forward secrecy” Forward secrecy comes from one-way hash functions. Examples of earlier hash designs include SHA-3, MD-5, SHA-I, and MAME. Each of these is having their proven record to produce the security for the communication between unconstrained devices. However, this is the era of Internet of Things (IoT) and the requirement of lightweight hash designs are the need of hour. IoT mainly consists of constrained devices. The devices in IoT are having many constrained related to battery power, storage and transmission range. Enabling any security feature in the constrained devices is troublesome. Constrained devices under an IoT environment can work only with less complex and lightweight algorithms. Lightweight algorithms take less power to operate and save a lot of energy of the battery operated devices. SPONGENT, QUARK, HASH-ONE, PHOTON, are some of the well-known lightweight hash designs currently providing security to the IoT devices. In this paper, the authors will present an analysis of the functioning of different lightweight hash designs as well as their suitability to the IoT environment.

Cryptographic algorithms are playing an important role in the information security field. Strong and unbreakable algorithms provide high security and good throughput. The strength of any encryption algorithm is basically based on the degree of difficulty to obtain the encryption key by such cyber-attacks as brute. It is supposed that the bigger the key size, the more difficult it is to compute the key. But increasing the key size will increase both the computational complexity and the processing time of algorithms. In this paper, we proposed a reliable, effective, and more secure symmetric stream cipher algorithm for encryption and decryption called Symmetric Cipher based on Key Hashing Algorithm (SCKHA). The idea of this algorithm is based on hashing and splitting the encryption symmetric key. Hashing the key will hide the encrypted key to prevent any intruder from forging the hash code, and, thus, it satisfies the purpose of security, authentication, and integrity for a message on the network. In addition, the algorithm is secure against a brute-force attack by increasing the resources it takes for testing each possible key. Splitting the hashed value of the encryption key will divide the hashed key into two key chunks. The encryption process performed using such one chunk based on some calculations on the plaintext. This algorithm has three advantages that are represented in computational simplicity, security and efficiency. Our algorithm is characterized by its ability to search on the encrypted data where the plaintext character is represented by two ciphertext characters (symbols).

This paper presents experimental results of evaluating the effect of network delay on the synchronization time of three parity machines. The possibility of using a hash function to confirm the synchronization of parity tree machines has been investigated. Three parity machines have been proposed as a modification of the symmetric encryption algorithm. One advantage of the method is the possibility to use the phenomenon of mutual synchronization of neural networks to generate an identical encryption key for users without the need to transfer it. As a result, the degree of influence of network delay and the type of hash function used on the synchronization time of neural networks was determined. The degree of influence of the network delay and hash function was determined experimentally. The hash function sha512 showed the best results. The tasks for further research have been defined.

We present an algorithm for generating perfect tabulation hashing functions by reduction to Boolean satisfaction (SAT). Tabulation hashing is a high-performance family of hash functions for hash tables that involves computing the XOR of random lookup tables. Given n keys of word size W, we show how to compute a perfect hash function in O(nW) worst-case time. This is competitive with other perfect hashing methods, and the resultant hash functions are simple and performant.

In recent years, hashing algorithm has been widely researched and has made considerable progress in large-scale image retrieval tasks due to its advantages of convenient storage and fast calculation efficiency. Nowadays most researchers use deep convolutional neural networks (CNNs) to perform feature learning and hash coding learning at the same time for image retrieval and the deep hashing methods based on deep CNNs perform much better than the traditional manual feature hashing methods. But most methods are designed to handle simple binary similarity and decrease quantization error, ignoring that the features of similar images and hashing codes generated are not compact enough. In order to enhance the performance of CNNs-based hashing algorithms for large scale image retrieval, this paper proposes a new deep-supervised hashing algorithm in which a novel channel attention mechanism is added and the loss function is elaborately redesigned to generate compact binary codes. It experimentally proves that, compared with the existing hashing methods, this method has better performance on two large scale image datasets CIFAR-10 and NUS-WIDE.

This work presents a novel security technique using the optical hash function to create a message digest algorithm in the wavelength domain. The optical devices used for high speed and high security algorithm handling comprised a PANDA ring resonator connected with an add/drop filter system. The PANDA ring resonator was introduced to access the dynamic behavior of bright-bright soliton collision within the modified add/drop filter. Outputs of the dynamic states formed key suppression as a high security application for optical cryptography. The add/drop filter was an essential device in the proposed design for optical hash function processing. Simulation outputs proved that the proposed technique obtained optical hash function in the wavelength domain for real time message digest creation. The wavelength of the data must be within 40% of the center wavelength of the system input signal. The integrity of the data was maintained by this highly secure process.

Image hash regimes have been widely used for authenticating content, recovery of images and digital forensics. In this article we propose a new algorithm for image haunting (SSL) with the most stable key points and regional features, strong against various manipulation of content conservation, including multiple combinatorial manipulations. In order to extract most stable keypoint, the proposed algorithm combines the Speed Up Robust Features (SURF) with Saliency detection. The keyboards and characteristics of the local area are then combined in a hash vector. There is also a sperate secret key that is randomly given for the hash vector to prevent an attacker from shaping the image and the new hash value. The proposed hacking algorithm shows that similar or initial images, which have been individually manipulated, combined and even multiple manipulated contents, can be visently identified by experimental result. The probability of collision between hacks of various images is almost nil. Furthermore, the key-dependent security assessment shows the proposed regime safe to allow an attacker without knowing the secret key not to forge or estimate the right havoc value.

Using the blockchain technology to store the privatedocuments of individuals will help make data more reliable and secure, preventing the loss of data and unauthorized access. The Consensus algorithm along with the hash algorithms maintains the integrity of data simultaneously providing authentication and authorization. The paper incorporates the block chain and the Identity Based Encryption management concept. The Identity based Management system allows the encryption of the user's data as well as their identity and thus preventing them from Identity theft and fraud. These two technologies combined will result in a more secure way of storing the data and protecting the privacy of the user.

Designing an efficient difficulty control algorithm is an essential problem in Proof-of-Work (PoW) based blockchains because the network hash rate is randomly changing. This paper proposes a general difficulty control algorithm and provides insights for difficulty adjustment rules for PoW based blockchains. The proposed algorithm consists a two-layer neural network. It has low memory cost, meanwhile satisfying the fast-updating and low volatility requirements for difficulty adjustment. Real data from Ethereum are used in the simulations to prove that the proposed algorithm has better performance for the control of the block difficulty.

Blockchain has received tremendous attention in non-monetary applications including the Internet of Things (IoT) due to its salient features including decentralization, security, auditability, and anonymity. Most conventional blockchains rely on computationally expensive validator selection and consensus algorithms, have limited throughput, and high transaction delays. In this paper, we propose tree-chain a scalable fast blockchain instantiation that introduces two levels of randomization among the validators: i) transaction level where the validator of each transaction is selected randomly based on the most significant characters of the hash function output (known as consensus code), and ii) blockchain level where validator is randomly allocated to a particular consensus code based on the hash of their public key. Tree-chain introduces parallel chain branches where each validator commits the corresponding transactions in a unique ledger.

For the purpose of outdoor advertising market researching, AD images are recaptured and uploaded everyday for statistics. But the quality of the recaptured advertising images are often affected by conditions such as angle, distance, and light during the shooting process, which consequently reduce either the speed or the accuracy of the retrieving algorithm. In this paper, we proposed a hash retrieval method based on convolutional neural networks for recaptured images. The basic idea is to add a hash layer to the convolutional neural network and then extract the binary hash code output by the hash layer to perform image retrieval in lowdimensional Hamming space. Experimental results show that the retrieval performance is improved compared with the current commonly used hash retrieval methods.

Image Retrieval (IR) has become one of the main problems facing computer society recently. To increase computing similarities between images, hashing approaches have become the focus of many programmers. Indeed, in the past few years, Deep Learning (DL) has been considered as a backbone for image analysis using Convolutional Neural Networks (CNNs). This paper aims to design and implement a high-performance image classifier that can be used in several applications such as intelligent vehicles, face recognition, marketing, and many others. This work considers experimentation to find the sequential model's best configuration for classifying images. The best performance has been obtained from two layers' architecture; the first layer consists of 128 nodes, and the second layer is composed of 32 nodes, where the accuracy reached up to 0.9012. The proposed classifier has been achieved using CNN and the data extracted from the CIFAR-10 dataset by the inception model, which are called the Transfer Values (TRVs). Indeed, the Particle Swarm Optimization (PSO) algorithm is used to reduce the TRVs. In this respect, the work focus is to reduce the TRVs to obtain high-performance image classifier models. Indeed, the PSO algorithm has been enhanced by using the crossover technique from genetic algorithms. This led to a reduction of the complexity of models in terms of the number of parameters used and the execution time.