Hash Function Research Articles

Quantum resource estimation for large scale quantum algorithms

Quantum algorithms are often represented in terms of quantum circuits operating on ideal (logical) qubits. However, the practical implementation of these algorithms poses significant challenges. Many quantum algorithms require a substantial number of logical qubits, and the inherent susceptibility to errors of quantum computers require quantum error correction. The integration of error correction introduces overhead in terms of both space (physical qubits required) and runtime (how long the algorithm needs to be run for). This paper addresses the complexity of comparing classical and quantum algorithms, primarily stemming from the additional quantum error correction overhead. We propose a comprehensive framework that facilitates a direct and meaningful comparison between classical and quantum algorithms. By acknowledging and addressing the challenges introduced by quantum error correction, our framework aims to provide a clearer understanding of the comparative performance of classical and quantum computing approaches. This work contributes to understanding the practical viability and potential advantages of quantum algorithms in real-world applications.We apply our framework to quantum cryptanalysis, since it is well known that quantum algorithms can break factoring and discrete logarithm based cryptography and weaken symmetric cryptography and hash functions. In order to estimate the real-world impact of these attacks, apart from tracking the development of fault-tolerant quantum computers it is important to have an estimate of the resources needed to implement these quantum attacks. This analysis provides state-of-the art snap-shot estimates of the realistic costs of implementing quantum attacks on these important cryptographic algorithms, assuming quantum fault-tolerance is achieved using surface code methods, and spanning a range of potential error rates. These estimates serve as a guide for gauging the realistic impact of these algorithms and for benchmarking the impact of future advances in quantum algorithms, circuit synthesis and optimization, fault-tolerance methods and physical error rates.

Efficient and Verifiable Range Query Scheme for Encrypted Geographical Information in Untrusted Cloud Environments

With the rapid development of geo-positioning technologies, location-based services have become increasingly widespread. In the field of location-based services, range queries on geographical data have emerged as an important research topic, attracting significant attention from academia and industry. In many applications, data owners choose to outsource their geographical data and range query tasks to cloud servers to alleviate the burden of local data storage and computation. However, this outsourcing presents many security challenges. These challenges include adversaries analyzing outsourced geographical data and query requests to obtain privacy information, untrusted cloud servers selectively querying a portion of the outsourced data to conserve computational resources, returning incorrect search results to data users, and even illegally modifying the outsourced geographical data, etc. To address these security concerns and provide reliable services to data owners and data users, this paper proposes an efficient and verifiable range query scheme (EVRQ) for encrypted geographical information in untrusted cloud environments. EVRQ is constructed based on a map region tree, 0–1 encoding, hash function, Bloom filter, and cryptographic multiset accumulator. Extensive experimental evaluations demonstrate the efficiency of EVRQ, and a comprehensive analysis confirms the security of EVRQ.

The paradigm logic of blockchain governance

Blockchain technology is mainly composed of public-key cryptography, hash algorithms and consensus mechanisms. Blockchain solves the two trust problems inherent in human interaction: the Byzantine Generals Problem and the Prisoner's Dilemma, which allows blockchain to replace the function of public power as a centre of trust to a certain extent, and also enables it to become a kind of decentralised large-scale collaborative infrastructure for human beings. Blockchain itself has a strong escape from public power, and the blockchain order is also non-rational. Its emergence has impacted the traditional constructivist-rationalism governance paradigm. Current research exploring blockchain governance issues at the level of governance paradigms is still limited. This study mainly adopts a normative analysis methodology, firstly outlining the decentralised technology model of blockchain and the economic ecosystem model built on top of this technology model. Secondly, this study explores the ontology of blockchain technology and its economic model on a philosophical level as well as the philosophical roots of the limitations of the traditional governance paradigm when confronted with blockchain governance. On this basis, this study further proposes ideas for shifting the blockchain governance paradigm. Specifically, it includes that a judicature-based governance paradigm should be established while the executive power should shift to focus on the issue of equality on the blockchain, and in the context of weakly centralised legislation, soft law governance should be allowed to play more of a role. Considering the popularity and development of blockchain technology, this study provides insights into the theoretical level of blockchain governance.

A QoS Analysis of SHA Algorithms for IoT Systems

Secure Hash Algorithms (SHA) are widely used in the Internet of Things (IoT) systems for message authentication and integrity verification. However, the performance of different SHA algorithms can vary significantly in terms of Quality of Service (QoS) metrics such as area utilization, processing speed, energy efficiency, and security. In this paper, we present a comprehensive analysis of the QoS parameters of various SHA algorithms and discuss the trade-offs between performance and security when selecting SHA algorithms for resource-constrained IoT devices. The study focuses on the hardware implementation of SHA algorithms in Field-Programmable Gate Array (FPGA) devices, which are commonly used in IoT applications. The performances and resource utilization of different SHA algorithms are compared and analyzed. The comparative results show that SHA-2 provide a good balance between performance and security, but SHA-3 provide better security due to its resistance to attacks such as length extension and collision

AI support for colonoscopy quality control using CNN and transformer architectures

BackgroundConstruct deep learning models for colonoscopy quality control using different architectures and explore their decision-making mechanisms.MethodsA total of 4,189 colonoscopy images were collected from two medical centers, covering different levels of bowel cleanliness, the presence of polyps, and the cecum. Using these data, eight pre-trained models based on CNN and Transformer architectures underwent transfer learning and fine-tuning. The models’ performance was evaluated using metrics such as AUC, Precision, and F1 score. Perceptual hash functions were employed to detect image changes, enabling real-time monitoring of colonoscopy withdrawal speed. Model interpretability was analyzed using techniques such as Grad-CAM and SHAP. Finally, the best-performing model was converted to ONNX format and deployed on device terminals.ResultsThe EfficientNetB2 model outperformed other architectures on the validation set, achieving an accuracy of 0.992. It surpassed models based on other CNN and Transformer architectures. The model’s precision, recall, and F1 score were 0.991, 0.989, and 0.990, respectively. On the test set, the EfficientNetB2 model achieved an average AUC of 0.996, with a precision of 0.948 and a recall of 0.952. Interpretability analysis showed the specific image regions the model used for decision-making. The model was converted to ONNX format and deployed on device terminals, achieving an average inference speed of over 60 frames per second.ConclusionsThe AI-assisted quality system, based on the EfficientNetB2 model, integrates four key quality control indicators for colonoscopy. This integration enables medical institutions to comprehensively manage and enhance these indicators using a single model, showcasing promising potential for clinical applications.

An Enhanced Blockchain-based Voting System Using Blake3 Cryptographic Hash Function

An Enhanced Blockchain-based Voting System Using Blake3 Cryptographic Hash Function

Secure discovery of genetic relatives across large-scale and distributed genomic data sets.

Finding relatives within a study cohort is a necessary step in many genomic studies. However, when the cohort is distributed across multiple entities subject to data-sharing restrictions, performing this step often becomes infeasible. Developing a privacy-preserving solution for this task is challenging owing to the burden of estimating kinship between all the pairs of individuals across data sets. We introduce SF-Relate, a practical and secure federated algorithm for identifying genetic relatives across data silos. SF-Relate vastly reduces the number of individual pairs to compare while maintaining accurate detection through a novel locality-sensitive hashing (LSH) approach. We assign individuals who are likely to be related together into buckets and then test relationships only between individuals in matching buckets across parties. To this end, we construct an effective hash function that captures identity-by-descent (IBD) segments in genetic sequences, which, along with a new bucketing strategy, enable accurate and practical private relative detection. To guarantee privacy, we introduce an efficient algorithm based on multiparty homomorphic encryption (MHE) to allow data holders to cooperatively compute the relatedness coefficients between individuals and to further classify their degrees of relatedness, all without sharing any private data. We demonstrate the accuracy and practical runtimes of SF-Relate on the UK Biobank and All of Us data sets. On a data set of 200,000 individuals split between two parties, SF-Relate detects 97% of third-degree or closer relatives within 15 h of runtime. Our work enables secure identification of relatives across large-scale genomic data sets.

A non-memory-based functional neural framework for animal caching behavior

The brain’s extraordinary abilities are often attributed to its capacity to learn and adapt. But memory has its limitations, especially when faced with tasks such as retrieving thousands of food items—a common behavior in scatter-hoarding animals. Here, we propose a brain mechanism that may facilitate caching and retrieval behaviors, with a focus on hippocampal spatial cells. Rather than memorizing the locations of their caches, as previously hypothesized, we suggest that cache-hoarding animals employ a static mechanism akin to hash functions commonly used in computing. Our mathematical model aligns with the activity of hippocampal spatial cells, which respond to an animal’s positional attention. We know that the region that activates each spatial cell remains consistent across subsequent visits to the same area but not between areas. This remapping, combined with the uniqueness of cognitive maps, produces persistent hash functions that can serve both food caching and retrieval. We present a simple neural network architecture that can generate such a probabilistic hash that is unique to the animal and not sensitive to environmental changes. This mechanism could serve a virtually boundless capacity for the encoding of any structured data.

SLAKA-IoD: A Secure and Lightweight Authentication and Key Agreement Protocol for Internet of Drones

The existing authentication and key agreement (AKA) schemes for the internet of drones (IoD) still suffer from various security attacks and fail to ensure required security properties. Moreover, drones generally have limited memory and computation capability. Motivated by these issues, a secure and lightweight AKA protocol for IoD (SLAKA-IoD) is proposed based on physical unclonable function (PUF), “exclusive or” (XOR) operation and hash function, which are simple cryptographic operations and functions that can provide better performance. In the SLAKA-IoD protocol, a drone and the ground station (GS) perform mutual authentication and establish a secure session key between them, and any two drones can also perform mutual authentication and establish a secure session key between them. Via informal security analysis, formal security analysis using the strand space model, and security verification based on the Scyther tool, the SLAKA-IoD protocol is proven to resist various security attacks and ensure required security properties. Further comparative analysis shows that the SLAKA-IoD protocol can provide more security features, and is generally lightweight as compared with these related AKA protocols for IoD, so it is suitable for IoD.

Lightweight hashing with contrastive self-cross XLNet and combinational similarity matching for content-based video retrieval

ABSTRACT Text-to-video retrieval systems enable simple and natural video searches, but existing methods often employ single queries with noisy annotation and low-quality descriptions. A novel lightweight hashing with Contrastive self-cross XLNet and Combinational similarity matching is proposed to provide high-quality descriptions and clarity in annotation. Issues in existing text and video processing approaches, such as redundancy in keyword generation and extracting frames without considering optical flow estimation of keyframes, are solved using a novel Textual Labelling and Automated key frame segmentation. The 3D Cuboid Net with Lightweight Hashing is proposed, which selects features and converts them into hash codes using Lightweight LSS-Net based hashing, reducing memory utilization. Additionally, a novel Combinational Attentive Text-Video Retrieval is proposed, which eliminates multiple narrow extrema matching error surfaces and increases retrieval validity. The model efficiently solves video retrieval problems with high accuracy, recall, and sensitivity. This novel approach addresses the limitations of existing text-to-video retrieval methods and improves the overall quality of the retrieval process.

ENHANCING DIGITAL TRANSACTIONS WITH BLOCKCHAIN TECHNOLOGY: DESCRIPTIVE-ANALYTICAL STUDY

Background: The emergence of Blockchain technology has led to profound transformations in digital transactions, offering a secure and transparent ledger for recording and processing transactions. This innovation holds promise for enhancing security, efficiency, and costeffectiveness across various sectors, including healthcare, education, finance, and real estate. Methods: This descriptive-analytical study explores the potential of Blockchain technology to revolutionise digital transactions. It employs a comprehensive review of existing literature and case studies to analyse the impact and applications of Blockchain across different domains. Results and conclusions: This research underscores the multifaceted benefits of Blockchain technology in streamlining processes, reducing transaction times, minimising fraud, and lowering costs across diverse industries. Blockchain emerges as a pioneering technology, functioning as the largest decentralised open database and facilitating transparent and secure data management. The technology, categorised into public, private, and hybrid types, comprises fundamental elements such as blocks, consensus mechanisms, cryptographic hash functions, and timestamps. With its core functions of transmission, storage, and automation, Blockchain disrupts conventional processes. Smart contracts, supported by external intermediaries like Oracle Programs, access data from external systems, enhancing their functionality and applicability. Moreover, Blockchain enables a departure from routine practices, ensuring robust monitoring of manufacturing processes, evaluating product quality, and verifying compliance with standards prior to market release.

EMAKAS: An efficient three-factor mutual authentication and key-agreement scheme for IoT environment

The adoption of IoT in healthcare revolutionizes remote patient monitoring and healthcare efficiency. Yet, it brings notable security and privacy challenges, particularly in resource-constrained environment. We propose a secure and efficient three-factor lightweight mutual authentication and key agreement scheme, designed for IoT-based smart healthcare systems, addressing these critical concerns. The scheme employs a fuzzy extractor, a one-way hash function, Elliptic Curve Discrete Logarithm and XOR operations for efficient cryptographic transformations, creating a robust framework for secure data handling. The scheme's design focuses on security and privacy while minimizing computational demands, making it ideal for resource-constrained IoT devices. We utilized both informal and formal security analyses to validate our scheme, employing the Random Oracle Model (ROM), Scyther tool and Burrows-Abadi-Needham (BAN) logic. The security and performance analysis showed that our scheme offers more security features across 15 defined criteria with minimal communication and computational costs compared to other related schemes. The scheme is not only robust against security threats but also practical for implementation in IoT healthcare environment, offering a solution for secure IoT communication by achieving mutual authentication and key agreement with minimized computational requirements.

A METHOD FOR SYNTHESIZING TOPOS OF HASHES IN THE CATEGORICAL cN-SCHEME OF THE MARKOV ALGORITHM. Part 1

In organizational systems, economic interactions are studied. The purpose of this study is to synthesize a problem-oriented management subsystem for combating money laundering and terrorist financing. To achieve this goal, the problem of synthesizing a constructive way of processing such interactions is solved. The method is based on the development of the N-scheme of the Markov algorithm with an output that cannot be considered normal according to Markov. This algorithm scheme allows us to consider a quadruple consisting of words in the ordered and extended Markov alphabet A2, morphism numbers N, source numbers i, and the alphabet M. From the N-scheme of the Markov algorithm, its categorical version is synthesized. The need for this scheme of the Markov algorithm is due to the solution of the problem of folding Markov occurrences of words into each other of large length for their more compact representation. The idea of organizing a convolution for the entire topos is tested separately. The term “topos of hashes” is introduced into scientific circulation. This topos is considered as an alternative way to represent blocks of chains of Markov occurrences. The hash topos is formed from hash trees known in blockchain technology. This idea follows from previous studies of classifiers in topos, as well as the possibility of splitting chains of Markov occurrences into blocks. The convolution is considered using gamma and hash functions. In the course of the study, it was possible to theoretically show the connection between Markov algorithms and blockchain technology, taking into account the fact that each chain of occurrences of words in each other is considered as a category, and these chains are formed in different sources, otherwise worlds, and the way they are synthesized may have signs of a creative process, then a connection was found between the theories of categories, algorithms, blockchain technology and intuitionistic models by S. Kripke and L. E. Ya. Brouwer.

Uniformity and Independence of H3 Hash Functions for Bloom Filters

Uniformity and Independence of H3 Hash Functions for Bloom Filters

MementoHash: A Stateful, Minimal Memory, Best Performing Consistent Hash Algorithm

MementoHash: A Stateful, Minimal Memory, Best Performing Consistent Hash Algorithm

RawHash2: mapping raw nanopore signals using hash-based seeding and adaptive quantization.

Raw nanopore signals can be analyzed while they are being generated, a process known as real-time analysis. Real-time analysis of raw signals is essential to utilize the unique features that nanopore sequencing provides, enabling the early stopping of the sequencing of a read or the entire sequencing run based on the analysis. The state-of-the-art mechanism, RawHash, offers the first hash-based efficient and accurate similarity identification between raw signals and a reference genome by quickly matching their hash values. In this work, we introduce RawHash2, which provides major improvements over RawHash, including more sensitive quantization and chaining algorithms, weighted mapping decisions, frequency filters to reduce ambiguous seed hits, minimizers for hash-based sketching, and support for the R10.4 flow cell version and POD5 and SLOW5 file formats. Compared to RawHash, RawHash2 provides better F1 accuracy (on average by 10.57% and up to 20.25%) and better throughput (on average by 4.0× and up to 9.9×) than RawHash. RawHash2 is available at https://github.com/CMU-SAFARI/RawHash. We also provide the scripts to fully reproduce our results on our GitHub page.

Tightly-Secure Two-Tier Signatures on Code-Based Digital Signatures with Chameleon Hash Functions

In the current landscape where quantum algorithms pose a significant threat to conventional digital signature algorithms, code-based digital signature algorithms have emerged as the primary focus of ongoing research in post-quantum cryptography. Digital signatures play a pivotal role in ensuring non-repudiation and authentication, making them an indispensable cryptographic technique. The vulnerability of most digital signature algorithms to quantum attacks have prompted a significant surge in research on code-based digital signature algorithms, which have emerged as a prominent field within post-quantum cryptography. There are generally three distinct approaches to constructing code-based digital signature algorithms: (1) Developing an algorithm that follows the inverse process of the code-based public-key encryption algorithm; (2) Utilizing zero-knowledge identification algorithms in conjunction with the Fiat–Shamir paradigm to formulate a signature algorithm; (3) Constructing a specialized subset of the syndrome space as the foundation for the digital signature algorithm. Chameleon Signature is a non-interactive signature that operates on the hash and signature paradigm, exhibiting comparable efficiency to conventional schemes. Its distinct advantage lies in the fact that the owner of the public key does not necessarily require access to the corresponding secret key within the Chameleon hash algorithm. Notably, Chameleon signatures possess an inherent characteristic of non-transferability, with their validity ascertainable solely by designated recipients. This paper introduces the first Chameleon hash function based on both KKS and HFE schemes, showcasing its superiority over traditional schemes through rank metrics and big fields for enhanced security. The deployment of Chameleon hash functions within hash-and-sign signature schemes introduces a nuanced layer of security and verification flexibility. This study elucidates the implications of integrating Chameleon hash functions into the recipient’s public key infrastructure, highlighting the dual capability it affords authorized parties for secure and adaptable verification processes, alongside mechanisms for the detection of unauthorized alterations.

Practical and secure policy-based chameleon hash for redactable blockchains

Abstract Policy-based chameleon hash functions have been widely proposed for its use in blockchain rewriting systems. They allow anyone to create a mutable transaction associated with an access policy, while an authorized user who possesses sufficient rewriting privileges from a trusted authority satisfying the access policy can rewrite the mutable transaction. However, existing chameleon hash functions lack certain fundamental security guarantees, including forward security and backward security. In this paper, we introduce a new primitive called forward/backward-secure policy-based chameleon hash (FB-PCH for short). We present a practical instantiation. We prove that the proposed scheme achieves forward/backward-secure collision-resistance, and show its practicality through implementation and evaluation analysis.

HCFAIUN: A novel hyperelliptic curve and fuzzy extractor-based authentication for secure data transmission in IoT-based UAV networks

IoT-based UAV networks comprise interconnected UAVs outfitted with sensors and microcontrollers to simplify data exchange in environments such as smart cities. In light of open-access communication landscapes, IoT-based UAV networks could pose security challenges, encompassing authentication vulnerabilities and the inadvertent disclosure of location and other confidential information to unauthorised parties. Henceforth, we have proposed a lightweight and secure authentication protocol: Hyperelliptic Curve and Fuzzy Extractor based Authentication in IoT-based UAV networks (HCFAIUN) leveraging Hyperelliptic Curve Cryptography(HCC), Fuzzy Extractor (FE), XOR operations and hash functions. HCC's maximum key size is 80 bits, differing from the 160-bit requirement of the elliptic curve, making it apt for UAVs with limited resources. The proposed scheme utilises biometrics traits of users to avoid exposing data from stealing smart devices using FE. This protocol facilitates the mutual authentication of users and UAVs, allowing them to exchange a session key for secure communication. The Hyperelliptic Curve (HC) scalar multiplication protects the user's private key from attackers, even in public channels. The obfuscation identity of the user and UAVs generated through the hash function and timestamp makes the external user and UAV anonymous. The efficacy of this proposed framework is examined using the Scyther verification tool and Random oracle model-based formal analysis, and informal analysis is also discussed, which validates its robustness against well-known potential physical and logical attacks. The performance analysis shows that the HCFAIUN scheme has lower computation, communication, and storage costs, i.e., 3.832 ms and 1456 bits and 1128 bits, respectively, compared to existing schemes.

A Privacy-Preserving Three-Factor Authentication System for IoT-Enabled Wireless Sensor Networks

Recently, Sahoo et al. introduced a three-factor authentication scheme for Wireless Sensor Networks (WSNs) based on an elliptic curve cryptosystem. Nonetheless, upon closer examination, we have identified critical vulnerabilities in their scheme, including susceptibility to user impersonation, gateway impersonation, sensor node impersonation attacks, and a breach in the three-factor security aspect. Further, the scheme fails to withstand offline sensor node identity guessing attacks, man-in-the-middle attacks, and known session-specific temporary information attacks. Intending to elevate both security and efficiency, we propose a novel three-factor authentication scheme that capitalizes on the strengths of a fuzzy extractor and a cryptographic one-way hash function. The proposed scheme’s security has been rigorously assessed using the SCYTHER tool, confirming its validity under the real-or-random (ROR) model. Moreover, a heuristic analysis exemplifies that the scheme effectively withstands various known cryptographic attacks. Consequently, the performance comparisons establish the superiority of our scheme over related approaches in terms of security and efficiency. Additionally, its suitability for WSNs is evident due to the minimal overhead on the sensor nodes, making it a highly promising solution for real-world implementation.