Hash Function Research Articles

Design and Implementation of Innovative Statistically Secured Hash Algorithm using Decentralized Hash Function

Information technology is in high demand today due to the expanding global use of the internet for information sharing. Every user wants to secure communication over a public network and to confirm the accuracy of their data. Hash algorithms are used to translate the fixed-length hash code from the input message, which is used to verify the integrity of the input message or any information exchanged through the web. Numerous hash algorithms are proposed by researchers but many algorithms use high consumption time during hash function processing time and they are using centralized hash function processing. Therefore we are designing an Innovative hash algorithm to generate a 96-bit hash code using a decentralized hash function.

PRACTICAL APPLICATIONS OF HASH FUNCTIONS MD5, SHA-1, AND SHA-256 USING VARIOUS SOFTWARE TOOLS TO VERIFY THE INTEGRITY OF FILES

In this scientific paper hash functions such as MD5, SHA-1, and SHA-256 are widely utilized in the field of cryptography to verify file integrity, confirming that files remain unaltered and free from corruption. The practical application of the hash functions MD5, SHA-1, AND SHA-256 through five widely used software tools: 7z, PowerShell, BullZip MD5 Calculator, HashCalc and CrypTool 1.4.40 is made. The analysis focuses on each tool's effectiveness in calculating and comparing hash values, emphasizing their strengths and weaknesses in ensuring data integrity. By investigating the functionality and applications of each tool, this scientific study provides important perspectives on the practical significance of MD5, SHA-1, and SHA-256 for maintaining the authenticity and reliability of digital files.

An Innovative Attention-based Triplet Deep Hashing Approach to Retrieve Histopathology Images.

Content-based histopathology image retrieval (CBHIR) can assist in the diagnosis of different diseases. The retrieval procedure can be complex and time-consuming if high-dimensional features are required. Thus, hashing techniques are employed to address these issues by mapping the feature space into binary values of varying lengths. The performance of deep hashing approaches in image retrieval is often superior to that of traditional hashing methods. Among deep hashing approaches, triplet-based models are typically more effective than pairwise ones. Recent studies have demonstrated that incorporating the attention mechanism into a deep hashing approach can improve its effectiveness in retrieving images. This paper presents an innovative triplet deep hashing strategy based on the attention mechanism for retrieving histopathology images, called histopathology attention triplet deep hashing (HATDH). Three deep attention-based hashing models with identical architectures and weights are employed to produce binary values. The proposed attention module can aid the models in extracting features more efficiently. Moreover, we introduce an improved triplet loss function considering pair inputs separately in addition to triplet inputs for increasing efficiency during the training and retrieval steps. Based on experiments conducted on two public histopathology datasets, BreakHis and Kather, HATDH significantly outperforms state-of-the-art hashing algorithms.

A novel binary hashing for agricultural scenery classification.

In this research, we present PerceptHashing, a technique designed to categorize million-scale agricultural scenic images by incorporating human gaze shifting paths (GSPs) into a hashing framework. For each agricultural image, we identify visually and semantically significant object patches, such as fields, crops, and water bodies. These patches are linked to form a graphlet, establishing a network of spatially adjacent patches, and a GSP is then extracted using an active learning algorithm. The GSP reflects the distribution of human gaze across different regions of each agricultural scene, typically involving fewer than 12 regions, as validated by cross-validation. We then design a binary hashing framework that effectively leverages the semantics encoded in these GSPs. This framework integrates three key elements: (i) refinement of noisy labels, (ii) incorporation of deep image-level agricultural semantics, and (iii) updates to the adaptive data graph. The resulting hash codes from each GSP are converted into a kernelized visual descriptor for classification. To evaluate the influence of GSPs on agricultural image classification both qualitatively and quantitatively, we conducted an extensive user study comparing GSPs from typical observers with those from Alzheimer's patients. The results demonstrate that: (1) the classification accuracy of 1.22 million agricultural aerial images using our approach is significantly higher than those processed by other methods, and (2) GSPs from 33 Alzheimer's patients differ markedly from those of 37 normal observers, leading to notable differences in classification accuracy ( versus ).

RELAKA: Robust ECC based Privacy Preserving Lightweight Authenticated Key Agreement protocol for healthcare applications

With the advancement of cutting-edge technologies, the Internet of Medical Things (IoMT) has assisted the healthcare sector by facilitating interaction between healthcare service providers and patients in remote areas. In IoMT, wearable or implantable sensors collect the patient’s record and share the information through a public network. Health-related information about the patient must be protected from a variety of attacks by the adversary since it is sensitive and extremely vulnerable to attacks. The sensor equipment that is implanted in the patient is also resource-constrained and has a low power capacity. The entities involved in the communication must be authenticated with one another in order to protect patients’ health information, anonymity, and reliability. While several authenticated key agreement protocols have been proposed, many suffer from high computational costs and storage cost, making them unsuitable for lightweight applications. This paper proposes a secure three-factor robust Elliptic Curve Cryptography (ECC) based mutually authenticated and key agreement protocol known as RELAKA for the IoMT environment, utilizing the benefits of one-way hash function. In proposed scheme, all entities, including the healthcare service providers and wearable sensors, are authenticated by the medical server. Subsequently, a secret key is established for each communication session and shared between all the entities. Additionally, mechanism for appropriate user revocation and re-registration is integrated to provide additional security in cases where a user’s QR code is tampered with by the attacker. The privacy of the proposed protocol is investigated by the potential use of zero knowledge proof. Furthermore, the efficacy of the authentication is examined by challenge and response mechanism. The informal security analysis demonstrates its resistance to threats such as DoS, impersonation, message modification, password guessing, and so on. The performance evaluation of RELAKA protocol indicates that the execution, communication, and storage costs is reduced by 87.59%, 43% and 60.71% respectively. Moreover, the outcomes of the AVISPA simulation illustrate that the RELAKA successfully evades both active and passive attacks. In addition, real-world testbed environment is developed with Raspberry pi 4 model B and the experimental results verifies the robustness of the proposed protocol. According to theoretical analysis and experimental evaluation, the RELAKA scheme is more secure and efficient than the existing protocols.

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>

Deep hashing and attention mechanism-based image retrieval of osteosarcoma scans for diagnosis of bone cancer

BackgroundDue to its intricate nature and substantial data size, microscopic image data of osteosarcoma often present a significant obstacle to the effectiveness of conventional image retrieval methods. Therefore, this study investigates a new approach for medical image retrieval using advanced deep hashing techniques and attention mechanisms to address these challenges more effectively. MethodThe proposed algorithm significantly improves osteosarcoma cell microscopic image retrieval efficiency and accuracy using deep hashing and attention mechanisms. Image preprocessing includes adaptive histogram equalization and dataset augmentation to enhance quality and diversity. Feature extraction employs the WRN-AM model to map high-dimensional features to a low-dimensional hash code space, improving retrieval efficiency. Finally, similarity matching via Hamming distance allows rapid and precise identification of similar images. ResultsThe study shows notable advancements: the WRN-AM model achieves 93.2% classification accuracy and 97.09% mAP using 64-bit hash codes. These findings underscore the technique’s effective performance in extracting and categorizing diverse microscopic cell data efficiently and reliably. ConclusionsThis innovative approach provides a robust solution for retrieving and classifying microscopic data of osteosarcoma cells and other cell types, speeding up clinical diagnosis and medical research. It facilitates quicker access and analysis of patient image data, enhancing diagnostic precision and treatment planning for healthcare professionals. Concurrently, it supports researchers in leveraging medical image data more efficiently, fostering progress and innovation in the medical field.

Redactable blockchain from Accountable Weight Threshold Chameleon Hash

The redactable blockchain provides the editability of blocks, which guarantees the data immutability of blocks while removing illegal content on the blockchain. However, the existing redactable blockchain relies on trusted assumptions regarding a single editing authority. Ateniese et al. (EuroS&P 2017) and Li et al. (TIFS 2023) proposed solutions by using threshold chameleon hash functions, but these lack accountability for malicious editing. This paper delves into this problem and proposes an accountability weight threshold blockchain editing scheme. Specifically, we first formalize the model of a redactable blockchain with accountability. Then, we introduce the novel concept of the Accountable Weight Threshold Chameleon Hash Function (AWTCH). This function collaboratively generates a chameleon hash trapdoor through a weight committee protocol, where only sets of committees meeting the weight threshold can edit data. Additionally, it incorporates a tracer to identify and hold accountable any disputing editors, thus enabling supervision of editing rights. We propose a generic construction for AWTCH. Then, we introduce an efficient construction of AWTCH and develop a redactable blockchain scheme by leveraging AWTCH. Finally, we demonstrate our scheme’s practicality. The editing efficiency of our scheme is twice that of Tian et al. (TIFS 2023) with the same number of editing blocks.

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.

A secure and privacy preserving model for healthcare applications based on blockchain-layered architecture

Blockchain is one of the emerging technologies that are applied in various fields and its application in Healthcare 4.0 is crucial to handle the vast amount of health records that are growing continuously everyday. This paper explores the application of blockchain technology-based layered architecture integrated with Interplanetary File System (IPFS) storage to provide a secure and robust sharing platform for healthcare system. A multilayer security is provided in the proposed system including a 128-bit Random Deoxyribonucleic Acid (DNA) encoding sequence with a modified version of the 128-bit Advanced Encryption Standard (AES) key expansion symmetric key technique is being used before storing the records in the IPFS storage, Rivest–Shamir–Adleman (RSA) 2048 digital envelope is used for secure transmission of symmetric keys, RSA 1024 is used for digital signature, and Secure Hash Algorithm 256-bit (SHA-256) is used for hashing the record files. Comparative analysis with existing state-of-the-art in blockchain-based E-healthcare system is discussed. The performance of various assessment metrics is evaluated, including encryption and decryption time for EHR records, mining time, record retrieval time, smart contract execution time and network latency. Additionally, a reputation-based filtering mechanism specifically designed in the proposed model to detect and mitigate the various attacks.

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