Hash Function Research Articles

Comparative study of password storing using hash function with MD5, SHA1, SHA2, and SHA3 algorithm

<span>The main purpose of passwords is to prevent unauthorized people from accessing the system. The rise in internet users has led to an increase in password hacking, which has resulted in a variety of problems. These issues include opponents stealing a company's or nation's private information and harming the economy or the organization's security. Password hacking is a common tool used by hackers for illegal purposes. Password security against hackers is essential. There are several ways to hack passwords, including traffic interception, social engineering, credential stuffing, and password spraying. In an attempt to prevent hacking, hashing algorithms are therefore mostly employed to hash passwords, making password cracking more difficult. In the suggested work, several hashing techniques, including message digest (MD5), secure hash algorithms (SHA1, SHA2, and SHA3) have been used. They have become vulnerable as a result of being used to store passwords. A rainbow table attack is conceivable. Passwords produced with different hash algorithms can have their hash values attacked with the help of the Hashcat program. It is proven that the SHA3 algorithm can help with more secure password storage when compared to other algorithms.</span>

Developing a nationwide dataset of UK veterans seeking help from sector charities

Abstract Background The assistance to military veterans and their families in the UK is provided by both the NHS and over 1800 military charities. These charities count services using different definitions and reporting systems, so to date a national registry of service usage does not exist. The aim of the MONARCH study is to build a standardised registry of service usage data for the military charity sector, in order to identify patterns of use, gain insight on possible risk factors, and guide the allocation of resources. Methods Data is anonymised and a unique identifier is generated by adopting a Secure Hashing Algorithm, allowing both privacy protection and avoiding double counts. Data is standardised, and linked with an automated process to create an aggregated dataset. The dataset describes the population, using both a-priori and machine learning approaches (K-means clustering) to unveil different usage patterns. In addition, it will be linked to an online interactive dashboard. Results To date 5 national charities have shared data, for a total of 42,509 veterans with 128,423 needs. The mean age of beneficiaries was 60.1 years (SD 20.5), and 90% were male. 65% were receiving some other form of statutory benefit, 5% was homeless and 1% was imprisoned. 65% of the needs recorded concerned social wellbeing. 40% of veterans were helped at least in two different years. The k-means clustering approach based on the number of accesses, number of needs and repetition of need returned 4 subgroups of use that were identical to those created using a priori knowledge. Conclusions The dataset is the most comprehensive source of charity usage data in the UK to date. Service usage is generally homogenous among subgroups, but some differences were highlighted indicating that younger, non-officer veterans may be more at risk of presenting with more complex needs. These first useful insights can help allocate resources to build an effective preventive strategy for more complex cases. Key messages • The MONARCH dataset is the first comprehensive nationwide registry in the UK of military charity data. • The understanding of usage patterns can lead to the design of targeted preventive strategies.

Text-Enhanced Graph Attention Hashing for Cross-Modal Retrieval

Deep hashing technology, known for its low-cost storage and rapid retrieval, has become a focal point in cross-modal retrieval research as multimodal data continue to grow. However, existing supervised methods often overlook noisy labels and multiscale features in different modal datasets, leading to higher information entropy in the generated hash codes and features, which reduces retrieval performance. The variation in text annotation information across datasets further increases the information entropy during text feature extraction, resulting in suboptimal outcomes. Consequently, reducing the information entropy in text feature extraction, supplementing text feature information, and enhancing the retrieval efficiency of large-scale media data are critical challenges in cross-modal retrieval research. To tackle these, this paper introduces the Text-Enhanced Graph Attention Hashing for Cross-Modal Retrieval (TEGAH) framework. TEGAH incorporates a deep text feature extraction network and a multiscale label region fusion network to minimize information entropy and optimize feature extraction. Additionally, a Graph-Attention-based modal feature fusion network is designed to efficiently integrate multimodal information, enhance the affinity of the network for different modes, and retain more semantic information. Extensive experiments on three multilabel datasets demonstrate that the TEGAH framework significantly outperforms state-of-the-art cross-modal hashing methods.

Research on time-division multiplexing for error correction and privacy amplification in post-processing of quantum key distribution

The post-processing of quantum key distribution mainly includes error correction and privacy amplification. The error correction algorithms and privacy amplification methods used in the existing quantum key distribution are completely unrelated. Based on the principle of correspondence between error-correcting codes and hash function families, we proposed the idea of time-division multiplexing for error correction and privacy amplification for the first time. That is to say, through the common error correction algorithms and their corresponding hash function families or the common hash function families and their corresponding error-correcting codes, error correction and privacy amplification can be realized by time-division multiplexing with the same set of devices. In addition, we tested the idea from the perspective of error correction and privacy amplification, respectively. The analysis results show that the existing error correction algorithms and their corresponding hash function families or the common privacy amplification methods and their corresponding error-correcting codes cannot realize time-division multiplexing for error correction and privacy amplification temporarily. However, according to the principle of correspondence between error-correcting codes and hash function families, the idea of time-division multiplexing is possible. Moreover, the research on time-division multiplexing for error correction and privacy amplification has some practical significance. Once the idea of time-division multiplexing is realized, it will further reduce the calculation and storage cost of the post-processing process, reduce the deployment cost of quantum key distribution, and help to remote the practical engineering of quantum key distribution.

Blockchain for video watermarking: An enhanced copyright protection approach for video forensics based on perceptual hash function.

As a direct result of advancements in digital technology and the Internet, the copyright protection and information integrity of multimedia that are being published across the Internet have emerged as a major and urgent issue that needs to be addressed. The technique of digital watermarking may be used to protect intellectual property. In terms of authentication, resilience, storage, and capacity of digital watermarking information, there is still room for development. Blockchain's potential in video copyright protection and management applications has motivated researchers. Copyright owners and consumers may now communicate directly via copyright protection apps built on the blockchain, eliminating the need for distributers and the associated fees. Nonetheless, the current blockchain-based video watermarking solutions require storing a significant number of coordinates depending on the watermark size and are susceptible to video frame attacks on the video frame texture region. This study proposes an enhanced video copyright management approach that incorporates digital watermarking, the blockchain, and a perceptual hash function. Watermark information is stored on a blockchain structure, which also acts as a timestamp for verification purposes. To verify watermark data without the source video, a perceptual hash function is employed to compute a hash value based on the structural information of video frames. The contribution is in learning how to extract a hash function from a small number of video frames that still adequately represent a large amount of video content while also reducing the number of unnecessary video frames and the amount of computation required to summarize and index that content in a blockchain. This expedites the dissemination of copyrighted works and increases their security and readability, hence facilitating their circulation on the Internet. Our experimental results demonstrate that this approach is memory efficient, as it only needs to store one key for each key frame, regardless of the size of the watermark. Additionally, the overall robustness is greatly improved by using the blockchain's random hash function. Therefore, new and important advancements in video watermarking have been realized because of this effort.

Analyzing Cryptographic Techniques and Machine Learning Algorithms for Crime Prediction

This report discusses cryptography techniques. Network security is defined as "keeping information hidden and secure from unauthorized users," whereas cryptography is defined as "the science of data protection." The Fundamental Requirements for Data Transmission are addressed in this work and as well as security attacks such as Data Transmission Interruption, Interception, and Modification. The Cryptographic Framework is explained using a generalized function, in which data is encrypted and decrypted using techniques such as the RSA algorithm, Hash Functions, and other cryptographic algorithms.SVM algorithms to analysis and predict the future cyber crime. This report introduces Cryptography Techniques. Cryptography is “The science of protecting data” & Network Security “keeping information private and Secure from unauthorized Users”. This paper gives the Fundamental Requirements for the Data Transmission, the security attacks like Interruption, Interception and Modification of the data Transmission. Keywords—cryptography, cybersecurity, machine learning, cybercrime prediction

Securing oil port logistics: A blockchain framework for efficient and trustworthy trade documents.

The oil port logistics involves multiple parties including oil tanker owners, port authorities, customs, oil suppliers, and shipping companies. These parties need to exchange a significant amount of data and documentation related to cargo, such as bills of lading, customs declarations, and cargo manifests. This huge amount of data and documentation provides ample opportunities for data manipulation and corruption. Moreover, physical documentation is slow and prone to errors and manipulation. This data can be securely stored and shared between different parties in a tamper-proof and transparent manner using blockchain. Blockchain is a decentralized technology that employs secure hashing and consensus algorithms that can detect any data modification. Hence, this work proposes a blockchain-enabled immutable, and efficient framework for trade documentation in oil port logistics. The proposed framework provides timely processing of oil trade documents and ensures immutability while increasing trust among the trade entities. In addition, this work implements a private blockchain for the execution of smart contracts, which can ensure that all parties involved in the logistics process comply with pre-agreed rules and regulations. Simulation results validate the effectiveness of the proposed framework in terms of transparency, immutability, network latency, throughput, and resource utilization.

Improvements in the randomness and security of digital currency using the photon sponge hash function through Maiorana–McFarland S-box replacement

Abstract There is a demand for a digital currency that facilitates remote trading over the Internet and strives to reduce external control, ensuring that transactions are conducted only between authorized persons or parties involved in the transfer. The financial sector has been significantly impacted by cryptocurrency, or digital currency, which brings new potential and challenges. The concept of digital currency is thoroughly examined, as are its complicated implications on various aspects of the economy. Trading digital currencies via the Internet may be vulnerable to theft and forgery due to the development of hacker programs. Therefore, we proposed to design a new digital currency and then built a 16 × 16 structure and filled the matrix with random numbers within GF(251). We used the random algorithm (Chun–Hui He’s iteration), then generated four directions, and used polynomial equations for the purpose of distributing powers between the parties in our future complete system. The original matrix was encoded with the photon sponge hash function after updating the S box by integrating the algorithms (Chun–Hui He’s iteration, Mariorana–McFarland method), and the results were good security measures (correlation coefficient, bijective property, balanced criteria, completeness criteria, and strict avalanche criteria) as well as the encryption and decryption time became faster (0.0005202). The major objective is to design a new digital currency system toward achieving security, scalability, and comprehensive adoption, looking into how it might improve security, promote financial inclusion, and change the present payment systems.

Block-based color image encryption algorithm by a novel memristor chaotic system and new RNA computation

The security of images is closely related to the protection of information privacy. We proposed a novel 5D memory resistive chaotic system (5D-MRCS), which exhibits good chaotic characteristics. Therefore, we employed it to design an image encryption algorithm aimed at ensuring secure image transmission. To further enhance the complexity of the algorithm and obtain more chaotic sequences, we combine the 5D-MRCS with the Hodgkin-Huxley (HH) model and use this combination in algorithm design. Initially, we combine the plain image with the hash function SHA-384 to devise and generate the secret key. Subsequently, the algorithm determines whether to pad the plain image based on different block size requirements. Then, we use multiple chaotic sequences generated by the 5D-MRCS and HH model to perform the global image permutation operation. Our designed permutation algorithm includes two parts: Block-based permutation and a new pixel-level permutation. Next, the scrambled image undergoes block-based random RNA diffusion, incorporating two newly proposed methods in the RNA operations, ultimately resulting in the ciphertext image. The algorithm’s NPCR, UACI, information entropy, and other security performance metrics are very close to the ideal values, and it possess characteristics such as resistance to differential, cutting, chosen plaintext, and noise attacks. Compared with other algorithms, it still has some advantages across multiple images and demonstrates excellent image encryption performance.

Taxonomic Data Objects for Communicating the Meaning of Species Names

A central problem in biodiversity data science remains the inability to precisely aggregate observations that originate from the same species. Current approaches still rely heavily on the ‘Genus species’ pair of Linnaean taxonomy to label and group data. However, such binomial species names do not alone contain enough information to distinguish variation in a name’s conceptual usage (i.e., meaning) across time, place, or investigator. This “species name-to-meaning” problem is well known, but no robust and scalable solutions yet exist (Berendsohn 1995Bisby 2000, Sutherland et al. 2000, Page 2007, Franz and Sterner 2018, Upham et al. 2021, Sterner et al. 2023, Sterner et al. 2020, Beach et al. 1993). The problem is widespread especially for well-studied groups like mammals, for which 45% more species are now recognized than 30 years ago, including many species splits and rearrangements (Burgin et al. 2018, Mammal Diversity Database 2024). To address this problem, we here propose to digitally package and sign the relevant taxonomic data associated with a given species name usage to form a ‘Taxonomic Data Object’ (TDO). These TDOs are conceptually similar to 'secundus' references (i.e., species name sec. author, Berendsohn 1995) but differ in being machine readable and thus operational from the perspective of high-throughput data science. TDOs aim to move from analog secundus references, which require a human to track meaning by reading the cited article, to a digital and machine-actionable taxonomic reference. Versioned TDOs that are digitally signed using hash algorithms (e.g., md5 or sha256, see Elliott et al. 2023) will allow for precisely communicating, with verified data provenance from sender to receiver, certain aspects of what a species name means according to a given authority at a given time. Example TDO contents include DarwinCore terms (e.g., scientificName, namePublishedIn, nameAccordingTo, nomenclaturalStatus (Darwin Core Maintenance Group 2021)) as well as meaning-rich digital assets like geographic range maps (e.g., GeoJSON format), exemplar DNA sequences (e.g., FASTA format or National Center for Biotechnology Information (NCBI) accession number), holotype specimen information (e.g., catalog number, type locality coordinates), and taxonomic treatment texts (including material citations, e.g., as digitized and extracted by Plazi TreatmentBank, Agosti and Egloff 2009; see Fig. 1). We discuss pilot examples comparing global bat species according to the v1.2 vs. v1.11 taxonomies of the Mammal Diversity Database (Sep 2020 vs. Apr 2023), showing how TDOs enable the reliable tracking of taxonomic meaning for the 63 bat species affected by splits during this period. We posit that widespread use of TDOs will enable the traceable exchange of taxonomic information across a variety of existing platforms, providing a path for accurate species-level data aggregation at global scales, at least for well-studied taxa.

Biometric CNN Model for Verification Based on Blockchain and Hyperparameter Optimization

Nowadays, fingerprints as biometrics are among the most popular means of identity verification for various applications. However, they are susceptible to theft, tampering, or alteration by attackers after storage. Hence, it is critical to guarantee the privacy of these fingerprint templates because standard privacy techniques are not secure enough. Additionally, fingerprint templates are verified using a deep learning model to distinguish between authentic and fake fingerprints, making them more protected and secure by storing them inside a blockchain, which has become the most common secure technique in recent years. This paper implements the proposed efficient and secure biometric system for verification based on blockchain technology and hyperparameter optimization. First, for the storing phase, each user’s authentic fingerprint template, private, and public keys are saved in the block and linked to the previous block in the chain by a hash function. If a hacker attempts to assault a fingerprint, all prior blocks must be changed. Second, for the authentication phase, the user logs in with his fingerprint and looks up the required template in the chain. If the required fingerprint exists, it creates a new block with the login details, and the number 1 is returned, which means that the authentication is valid; if it does not exist, it is verified to see if it is authentic or fake using the proposed biometric convolutional neural network (CNN). The proposed CNN uses the Grid Search (GS) algorithm to tune hyperparameters to distinguish between authentic and fake fingerprints. The SOCOFing dataset is used for evaluating our experiment. According to the experimental results, the proposed CNN model achieved the highest accuracy of 99.52%. As a result of the blockchain, our system can return authentication information after looking up the chain in 300 ms.

Design and Implementation of Hardware-Software Architecture Based on Hashes for SPHINCS+

Advances in quantum computing have posed a future threat to today’s cryptography. With the advent of these quantum computers, security could be compromised. Therefore, the National Institute of Standards and Technology (NIST) has issued a request for proposals to standardize algorithms for post-quantum cryptography (PQC), which is considered difficult to solve for both classical and quantum computers. Among the proposed technologies, the most popular choices are lattice-based (shortest vector problem) and hash-based approaches. Other important categories are public key cryptography (PKE) and digital signatures. Within the realm of digital signatures lies SPHINCS+. However, there are few implementations of this scheme in hardware architectures. In this article, we present a hardware-software architecture for the SPHINCS+ scheme. We utilized a free RISC-V (Reduced Instruction Set Computer) processor synthesized on a Field Programmable Gate Array (FPGA), primarily integrating two accelerator modules for Keccak-1600 and the Haraka hash function. Additionally, modifications were made to the processor to accommodate the execution of these added modules. Our implementation yielded a 15-fold increase in performance with the SHAKE-256 function and nearly 90-fold improvement when using Haraka, compared to the reference software. Moreover, it is more compact compared to related works. This implementation was realized on a Xilinx FPGA Arty S7: Spartan-7.

Lower Bound on Number of Compression Calls of a Collision-Resistance Preserving Hash

The collision-resistant hash function is an early cryptographic primitive that finds extensive use in various applications. Remarkably, the Merkle-Damgård and Merkle tree hash structures possess the collision-resistance preserving property, meaning the hash function remains collision-resistant when the underlying compression function is collision-resistant. This raises the intriguing question of whether reducing the number of underlying compression function calls with the collision-resistance preserving property is possible. In pursuit of addressing these inquiries, we prove that for an ℓ n -to- s n -bit collision-resistance preserving hash function designed using r t n -to- n -bit compression function calls, we must have r ≥ ⌈ ( ℓ − s ) / ( t − 1 ) ⌉ . Throughout the paper, all operations other than the compression function are assumed to be linear (which we call linear hash mode).

Secure and Transparent Craftwork Authentication and Transaction System: Integrating Digital Fingerprinting and Blockchain Technologies

This study proposes a method that enables craftsmen to define and apply the unique characteristics of their craftworks to distinguish between originals and imitations and to protect and trade their intellectual property rights. In the first step, a digital fingerprint that enables the authentication of the original craftworks was generated by applying hash functions that can digitize various attributes of the craftworks and create a unique ID. In the second step, a blockchain transaction system for the original authentication of the craftwork was developed by applying consortium blockchain technology. This system allows multiple craft-related organizations to participate together, and when a transaction occurs, a smart contract is created and stored on the blockchain, thereby enabling the tracking and management of transaction histories. Furthermore, a DApp was developed that enables buyers to verify the craftwork authentication and access detailed information by scanning the digital fingerprint (QR code) of the craftwork, which is integrated with the blockchain system. In the third step, the research results were evaluated through a satisfaction survey conducted with 121 participants and a usability evaluation with 10 craftsmen, both of which yielded positive feedback. This study successfully realizes a secure and transparent craftwork transaction system that guarantees both security and efficiency through the integration of digital fingerprinting and blockchain technologies.

Cryptanalysis of TS-Hash

This note presents attacks on the lightweight hash function TS-Hash proposed by Tsaban, including a polynomial-time preimage attack for short messages (at most n / 2 bits), high-probability differentials, a general subexponential-time preimage attack, and linearization techniques.

Efficient Boolean-to-Arithmetic Mask Conversion in Hardware

Masking schemes are key in thwarting side-channel attacks due to their robust theoretical foundation. Transitioning from Boolean to arithmetic (B2A) masking is a necessary step in various cryptography schemes, including hash functions, ARX-based ciphers, and lattice-based cryptography. While there exists a significant body of research focusing on B2A software implementations, studies pertaining to hardware implementations are quite limited, with the majority dedicated solely to creating efficient Boolean masked adders. In this paper, we present first- and second-order secure hardware implementations to perform B2A mask conversion efficiently without using masked adder structures. We first introduce a first-order secure low-latency gadget that executes a B2A2k in a single cycle. Furthermore, we propose a second-order secure B2A2k gadget that has a latency of only 4 clock cycles. Both gadgets are independent of the input word size k. We then show how these new primitives lead to improved B2Aq hardware implementations that perform a B2A mask conversion of integers modulo an arbitrary number. Our results show that our new gadgets outperform comparable solutions by more than a magnitude in terms of resource requirements and are at least 3 times faster in terms of latency and throughput. All gadgets have been formally verified and proven secure in the glitch-robust PINI security model. We additionally confirm the security of our gadgets on an FPGA platform using practical TVLA tests.

An Image Encryption Algorithm Based on Tabu Search and Hyperchaos

In this paper, we propose a digital image encryption scheme based on Tabu Search (TS) algorithm and Chen’s hyperchaos system. First, in order to enhance the security of the algorithm and resist the known-plaintext attack, the key is associated with the ordinary image, and the hash value generated by the ordinary image is used as the initial value of the hyperchaotic system. Moreover, the TS algorithm is used to obtain the optimal subsequence to scramble the sub-block image, which ensures the scrambling effect of the algorithm. In addition, for the sake of minimizing the correlation of neighborhood pixels and strengthening the effect of scrambling, the ordinary image is divided into blocks for scrambling and diffusion. Through simulation and experiments, the key sensitivity, differential attack and pure ciphertext attack are analyzed. Compared with the other encryption schemes, the results verify the effectiveness and reliability of the proposed scheme.

Protocol for Designing De Novo Noncanonical Peptide Binders in OSPREY.

D-peptides, the mirror image of canonical L-peptides, offer numerous biological advantages that make them effective therapeutics. This article details how to use DexDesign, the newest OSPREY-based algorithm, for designing these D-peptides de novo. OSPREY physics-based models precisely mimic energy-equivariant reflection operations, enabling the generation of D-peptide scaffolds from L-peptide templates. Due to the scarcity of D-peptide:L-protein structural data, DexDesign calls a geometric hashing algorithm, Method of Accelerated Search for Tertiary Ensemble Representatives, as a subroutine to produce a synthetic structural dataset. DexDesign enables mixed-chirality designs with a new user interface and also reduces the conformation and sequence search space using three new design techniques: Minimum Flexible Set, Inverse Alanine Scanning, and K*-based Mutational Scanning.

A network security protection scheme for tax system based on elliptic curve cryptography

With the rapid development of modern information technology and network technology, the construction of tax informatization plays an important role in improving the level of tax management and work efficiency. However, in the centralized data processing mode, the establishment of large data center and the efficient, stable, and secure connection of server bandwidth have become key challenges. Moreover, the openness and interconnectivity of the tax system network pose various network security threats, seriously threatening the integrity and security of data. For the above issues, this article proposes a tax system network security protection scheme based on elliptic curve cryptography. This scheme combines hash function and elliptic curve cryptography to achieve user anonymity and secure generation of session keys, effectively ensuring the secure transmission of confidential information. The security and effectiveness of the scheme are verified through informal and formal security analysis based on security proof, BAN logic and AVISPA tool. And a comprehensive evaluation of the scheme is conducted, and the results show that the scheme significantly reduced the costs while providing security features.

Anomaly Detection over Streaming Graphs with Finger-Based Higher-Order Graph Sketch

A streaming graph is a constantly growing sequence of edges, which forms a dynamic graph that changes with every edge in the stream. An anomalous behavior in a streaming graph can be modeled as an edge or a subgraph that is unusual compared to the rest of the graph. Identifying anomalous behaviors in real time is essential to the early warning of abnormal or notable events. Due to the complexity of the problem, little work has been reported so far to solve the problem. In this paper, we propose Finger-based Higher-order Graph Sketch (FHGS for short), which is an approximate data structure for streaming graphs with linear memory usage, high update speed, and high accuracy and supports both edge and subgraph anomaly detection. FHGS first maps each edge into a matrix based on hash functions, and then counts its frequency in a time window with unique fingerprints for detecting anomalies. Extensive experiments confirm that our approach generate high-quality results compared to baseline methods.