Hash Function Research Articles

JumpBackHash: Say Goodbye to the Modulo Operation to Distribute Keys Uniformly to Buckets

ABSTRACTIntroductionDistributed data processing and storage systems require efficient methods to distribute keys across buckets. While simple and fast, the traditional modulo‐based mapping is unstable when the number of buckets changes, leading to spikes in system resource utilization, such as network or database requests. Consistent hash algorithms minimize remappings but are either significantly slower, require floating‐point arithmetic, or are based on a family of hash functions rarely available in standard libraries. This work introduces JumpBackHash, a consistent hash algorithm that overcomes those shortcomings.MethodologyJumpBackHash applies the concept of active indices borrowed from consistent weighted sampling, which inherently leads to consistency. It generates the active indices in reverse order, which avoids floating‐point operations, enables the minimization of consumed random values and the use of a standard pseudorandom generator, and finally leads to a very efficient algorithm.ResultsTheoretical analysis shows that JumpBackHash has an expected constant runtime. The expected value and the variance of the number of consumed random values perfectly agree with the experiments. Empirical tests also confirm the consistency.ConclusionJumpBackHash offers a fast and efficient solution for uniformly distributing keys across buckets in distributed systems. Its simplicity, performance, and the availability of a production‐ready Java implementation as part of the Hash4j open source library make it a viable replacement for the modulo‐based approach for improving assignment and system stability.

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>

LPSRUA–MIoT: Lightweight and Provable Secure Remote User Authentication Scheme for Multi‐Gateway Computing Framework in Internet of Things

ABSTRACTInternet of Things (IoT) has evolved into a new era of information and communication technology where several day‐to‐day usable physical objects have been interconnected to form an intelligent ad‐hoc network that is deployed into various hostile environments to monitor, gather and process the surveillance data. However, there is a huge potential gap between communication in traditional wired network and wireless IoT network. IoT network is susceptible to several security threats due to their distributed and decentralized architecture, open nature of communication channel, dynamic nodal infrastructure, high possibilities of malicious penetrations, and so forth. In this scenario, a secure, efficient, and lightweight remote user authentication and key agreement scheme can play a significant role to mitigate the above‐mentioned issues and to provide well‐protected communication between the communicating entities in the IoT environment. Therefore in this paper, we have proposed a provable secure, robust and lightweight biometric‐enabled mutual authentication scheme for a multi‐gateway IoT environment using an irreversible hash function and fuzzy extractor. To support our claim, we have formally verified our scheme using the broadly accepted Random Oracle Model (ROM) and further simulated using the widely used AVISPA simulation tool. Additionally, the performance analysis in terms of computation and communication overheads of the proposed scheme is compared with other existing relevant authentication schemes that clearly shows that the proposed protocol maintains an acceptable efficiency security trade‐off for implementation in real‐life scenarios.

Enhancing Blockchain Security by Developing the SHA256 Algorithm

Security plays a vital role in various domains, including blockchain technology. The Blockchain serves as a secure data structure for storing transactional records. Hash functions are employed in cryptography to ensure integrity and authentication within the blockchain. The widely used SHA256 algorithm has faced recent attacks, prompting the development of stronger hash functions. This paper presents a novel modification approach to enhance the performance of SHA256 by introducing an extended mechanism for generating a 288-bit message digest and reducing the number of rounds to 44 instead of 64 while preserving the diffusion of data through its complex iterative process, which involves multiple rounds of bitwise and logical operations. The change makes sure that even small changes to the input data cause noticeable variations in the output hash, thereby maintaining cryptographic properties. The suggested hash function SHA288 achieves improved security, collision resistance, and preimage resistance, while maintaining a faster execution time compared to SHA256. The tables and tests conducted on the suggested algorithm have revealed its remarkable safety and robustness in countering attacks as well as demonstrated outstanding performance in random tests, which further enhances its security measures.

A Versatile Image Encryption Scheme Based on Optical Hologram Technology and Chess Rules

A novel versatile image encryption scheme about hologram technology and chess rules is proposed in this paper. This scheme can encrypt images by transferring them from the spatial domain to the frequency domain. First, the theory of computer-generated holograms is applied to record phase and amplitude features by imitating the optical hologram. The original image is transformed to a hologram and the phase values of the points in the image are recorded. Second, the parameters associated with the plaintext image are obtained by a Hash function and used as the initial values of the chaotic system, and then the pawns on the board are regarded as pixel points of the image, where the movement direction of the pawns is the movement direction of the pixel points of the image. The hologram is confused by combining the pawns’ movement rules with the chaotic sequences. Finally, the encrypted image is gained through a combination of the chaotic sequence and the selective diffusion method. The test results show that the scheme is able to resist various attacks and has good security performance in encryption.

LostNet: A smart way for lost and find.

The rapid population growth in urban areas has led to an increased frequency of lost and unclaimed items in public spaces such as public transportation, restaurants, and other venues. Services like Find My iPhone efficiently track lost electronic devices, but many valuable items remain unmonitored, resulting in delays in reclaiming lost and found items. This research presents a method to streamline the search process by comparing images of lost and recovered items provided by owners with photos taken when items are registered as lost and found. A photo matching network is proposed, integrating the transfer learning capabilities of MobileNetV2 with the Convolutional Block Attention Module (CBAM) and utilizing perceptual hashing algorithms for their simplicity and speed. An Internet framework based on the Spring Boot system supports the development of an online lost and found image identification system. The implementation achieves a testing accuracy of 96.8%, utilizing only 0.67 GFLOPs and 3.5M training parameters, thus enabling the recognition of images in real-world scenarios and operable on standard laptops.

Message Authentication Scheme on Light Weight Parallel Encryption Model With Digit Artithmetic of Covertext using Secure Hash Algorithm

This research aims to enhance message security by developing a Light Weight Parallel Digit Arithmetic of Covertext (PDAC) encryption-based authentication scheme that adopts the Secure Hash Algorithm (SHA) algorithm to ensure message integrity and authentication aspects. Through experiments, tests are conducted on two main aspects: entropy and collision resistance. The experimental results show that the average entropy generated reaches 4.992, or 62.41% of the optimal entropy value. While this result shows a good randomness, there is room for further improvement to reach the optimal level. Collision resistance testing using Google's SHAttered tool showed very positive results. No collisions were found in any of the samples tested, indicating that the PDAC scheme combined with SHA can maintain message integrity well. This indicates that PDAC has strong resistance to hash-based attacks, ensuring that messages remain authentic and safe from manipulation. Thus, this study concludes that PDAC schemes offer a good balance between efficiency, security, and low power consumption, making it suitable for use in resource-constrained environments such as IoT devices and mobile applications

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.