Number Of Rounds Research Articles

Audio File Cryptography using Variable Number of Rounds for Blocks Substitution

An efficient method of audio file cryptography will be introduced, the method will decrease both the encryption and decryption times comparing with other existing methods, the method will speed up the process of audio file cryptography. The introduced method will use a variable number of rounds for audio file cryptography, each round will use a block size, number of blocks and generated indices keys, these parameters will be variable and change from round to another. The private key will have a variable length, and it will depend on the selected number of rounds (192 bits for each round), this length will be good enough to produce a huge key space capable to resist hacking attacks, the produced decrypted file will be very sensitive to the selected values of the private key. The introduced method will be simple and easy to implement, it will use a simple chaotic logistic map model to generate the required for each round indices key. The introduced method will use a simple substitution operation to apply audio file samples substitution without altering the samples values and without using any extra logical and arithmetic operations (required for other existing methods). The introduced method will be tested and implemented using mat lab; the obtained results will be used to show that the method will satisfy the requirements of good crypto method.

Asymmetric secure multi-party quantum computation with weak clients against dishonest majority

Abstract Secure multi-party computation (SMPC) protocols allow several parties distrusting each other to collectively compute a function on their inputs. In this paper, we introduce a protocol that lifts SMPC to its quantum counterpart — Secure multi-party quantum computation (SMPQC) for classical inputs and outputs — in a composable and statistically secure way, even for a single honest party. The security error is shown to be inverse polynomial with respect to the number of rounds in the protocol while it can be boosted to negligible for BQP computations. Unlike previous SMPQC protocols, our proposal only requires very limited quantum resources from all but one party. In addition, the protocol exhibits some noise robustness that can facilitate small-scale implementations with near-future technologies.

Emergent numeracy: How the crowd wisdom of non-rounding survey respondents generates accurate immigration estimates

Survey respondents overestimate, on average, the number of immigrants living in their country. This phenomenon, known as immigration innumeracy, seems incompatible with the wisdom-of-crowds effect, which suggests that large samples of individuals should, as a collectivity, be good at estimating such figures. However, this article reveals that crowd wisdom does emerge in a previously overlooked subgroup: non-rounding respondents. Drawing on German and European survey data, it is shown that when round estimates (5, 10, 15, …) are excluded, a subset of “wise” non-rounding respondents remains, who collectively estimate the share of foreigners in their country with astounding precision. Thus, a relevant—and easily identifiable—part of the population is actually collectively immigration numerate. Two potential mechanisms behind this emergent phenomenon are explored. First, regression models reveal that non-rounders are more educated and politically interested than rounders, indicating more informed guesses. Second, simulations show that the coarse-grained nature of round numbers itself can contribute to inaccurate estimates. Using round numbers is thus associated with uncertainty, resulting in biased estimates. Taking the crowd wisdom of non-rounding respondents into account qualifies the extent of immigration innumeracy and improves our understanding of a phenomenon that is often seen as a root cause of xenophobia.

Round-Optimal Compiler for Semi-Honest to Malicious Oblivious Transfer via CIH

A central question in the theory of cryptography is whether we can build protocols that achieve stronger security guarantees, e.g., security against malicious adversaries, by combining building blocks that achieve much weaker security guarantees, e.g., security only against semi-honest adversaries; and with the minimal number of rounds. An additional focus is whether these building blocks can be used only as a black-box. Since Oblivious Transfer (OT) is the necessary and sufficient building block to securely realize any two-party (and multi-party) functionality, theoreticians often focus on proving whether maliciously secure OT can be built from a weaker notion of OT. There is a rich body of literature that provides (black-box) compilers that build malicious OT from OTs that achieve weaker security such as semi-malicious OT and defensibly secure OT, within the minimal number of rounds. However, no round-optimal compiler exists that builds malicious OT from the weakest notion of semi-honest OT, in the plain model. Correlation intractable hash (CIH) functions are special hash functions whose properties allow instantiating the celebrated Fiat-Shamir transform, and hence reduce the round complexity of public-coin proof systems. In this work, we devise the first round-optimal compiler from semi-honest OT to malicious OT, by a novel application of CIH for collapsing rounds in the plain model. We provide the following contributions. First, we provide a new CIH-based round-collapsing construction for general cut-and-choose. This gadget can be used generally to prove the correctness of the evaluation of a function. Then, we use our gadget to build the first round-optimal compiler from semi-honest OT to malicious OT. Our compiler uses the semi-honest OT protocol and the other building blocks in a black-box manner. However, for technical reasons, the underlying CIH construction requires the upper bound of the circuit size of the semi-honest OT protocol used. The need for this upper-bound makes our protocol not fully black-box, hence is incomparable with existing, fully black-box, compilers.

The effect of exploiting the public good on climate cooperation: evidence from a collective-social risk dilemma experiment

AbstractReaching ambitious climate targets is challenging, due the individual incentives of countries to free ride and to continue contributing to climate change. This issue of climate change has been analyzed in collective-risk social dilemma (CRSD) experiments, where participants interact in groups and can invest money in a group account over a fixed number of rounds. If the group account is below a threshold after the last round, the group members lose a large proportion of their assets. In the real world, however, agents can not only invest in public goods, but also exploit them. We argue that this possibility reduces the likelihood to reach the threshold. To test this prediction, we introduce the option to also exploit the public good in a CRSD experiment. The results reveal that a take option negatively affects the likelihood of reaching the threshold. Overall, the effect of the existence of a take option is rather small and not statistically significant. However, if participants exert their option to exploit the public good at the beginning, the success rate drops significantly. Consistent with the results of previous studies without the option of exploiting the public good, we find that a lower loss rate makes cooperation less likely, but the effect of heterogeneity is less clear. Our findings indicate that CRSD experiments that do not consider exploitation are likely to overestimate the likelihood of successful cooperation. The key implication for policy is to focus on reducing the incentives of the take option.

Efficient federated unlearning under plausible deniability

Privacy regulations like the GDPR in Europe and the CCPA in the US allow users the right to remove their data from machine learning (ML) applications. Machine unlearning addresses this by modifying the ML parameters in order to forget the influence of a specific data point on its weights. Recent literature has highlighted that the contribution from data point(s) can be forged with some other data points in the dataset with probability close to one. This allows a server to falsely claim unlearning without actually modifying the model’s parameters. However, in distributed paradigms such as federated learning (FL), where the server lacks access to the dataset and the number of clients are limited, claiming unlearning in such cases becomes a challenge. An honest server must modify the model parameters in order to unlearn. This paper introduces an efficient way to achieve machine unlearning in FL, i.e., federated unlearning, by employing a privacy model which allows the FL server to plausibly deny the client’s participation in the training up to a certain extent. Specifically, we demonstrate that the server can generate a Proof-of-Deniability, where each aggregated update can be associated with at least x (the plausible deniability parameter) client updates. This enables the server to plausibly deny a client’s participation. However, in the event of frequent unlearning requests, the server is required to adopt an unlearning strategy and, accordingly, update its model parameters. We also perturb the client updates in a cluster in order to avoid inference from an honest but curious server. We show that the global model satisfies (ϵ,δ)-differential privacy after T number of communication rounds. The proposed methodology has been evaluated on multiple datasets in different privacy settings. The experimental results show that our framework achieves comparable utility while providing a significant reduction in terms of memory (≈ 30 times), as well as retraining time (1.6-500769 times). The source code for the paper is available https://github.com/Ayush-Umu/Federated-Unlearning-under-Plausible-Deniability.

Stabilizing and Accelerating Federated Learning on Heterogeneous Data With Partial Client Participation.

Federated learning (FL) commonly encourages the clients to perform multiple local updates before the global aggregation, thus avoiding frequent model exchanges and relieving the communication bottleneck between the server and clients. Though empirically effective, the negative impact of multiple local updates on the stability of FL is not thoroughly studied, which may result in a globally unstable and slow convergence. Based on sensitivity analysis, we define in this paper a local-update stability index for the general FL, as measured by the maximum inter-client model discrepancy after the multiple local updates that mainly stems from the data heterogeneity. It enables to determine how much the variation of client's models with multiple local updates may influence the global model, and can also be linked with the convergence and generalization. We theoretically derive the proposed local-update stability for current state-of-the-art FL methods, providing possible insight to understanding their motivation and limitation from a new perspective of stability. For example, naively executing the parallel acceleration locally at clients would harm the local-update stability. Motivated by this, we then propose a novel accelerated yet stabilized FL algorithm (named FedANAG) based on the server- and client-level Nesterov accelerated gradient (NAG). In FedANAG, the global and local momenta are elaborately designed and alternatively updated, while the stability of local update is enhanced with help of the global momentum. We prove the convergence of FedANAG for strongly convex, general convex and non-convex settings. We then conduct evaluations on both the synthetic and real-world datasets to first validate our proposed local-update stability. The results further show that across various data heterogeneity and client participation ratios, FedANAG not only accelerates the global convergence by reducing the required number of communication rounds to a target accuracy, but converges to an eventually higher accuracy.

The 2 -burning Number of a Graph

We study a discrete-time model for the spread of information in a graph, motivated by the idea that people believe a story when they learn of it from two different origins. Similar to the burning number, in this problem, information spreads in rounds and a new source can appear in each round. For a graph , we are interested in , the minimum number of rounds until the information has spread to all vertices of graph . We are also interested in finding , the minimum number of sources necessary so that the information spreads to all vertices of in rounds. In addition to general results, we find and for the classes of spiders and wheels and show that their behavior differs with respect to these two parameters. We also provide examples and prove upper bounds for these parameters for Cartesian products of graphs.

High-throughput gene expression analysis with TempO-LINC sensitively resolves complex brain, lung and kidney heterogeneity at single-cell resolution

We report the development and performance of a novel genomics platform, TempO-LINC, for conducting high-throughput transcriptomic analysis on single cells and nuclei. TempO-LINC works by adding cell-identifying molecular barcodes onto highly selective and high-sensitivity gene expression probes within fixed cells, without having to first generate cDNA. Using an instrument-free combinatorial indexing approach, all probes within the same fixed cell receive an identical barcode, enabling the reconstruction of single-cell gene expression profiles across as few as several hundred cells and up to 100,000 + cells per sample. The TempO-LINC approach is easily scalable based on the number of barcodes and rounds of barcoding performed; however, for the experiments reported in this study, the assay utilized over 5.3 million unique barcodes. TempO-LINC offers a robust protocol for fixing and banking cells and displays high-sensitivity gene detection from multiple diverse sample types. We show that TempO-LINC has a multiplet rate of less than 1.1% and a cell capture rate of ~ 50%. Although the assay can accurately profile the whole transcriptome (19,683 human, 21,400 mouse and 21,119 rat genes), it can be targeted to measure only actionable/informative genes and molecular pathways of interest – thereby reducing sequencing requirements. In this study, we applied TempO-LINC to profile the transcriptomes of more than 90,000 cells across multiple species and sample types, including nuclei from mouse lung, kidney and brain tissues. The data demonstrated the ability to identify and annotate more than 50 unique cell populations and positively correlate expression of cell type-specific molecular markers within them. TempO-LINC is a robust new single-cell technology that is ideal for large-scale applications/studies with high data quality.

Characterization of errors in a CNOT between surface code patches

As current experiments already realize small quantum circuits on error corrected qubits, it is important to fully understand the effect of physical errors on the logical error channels of these fault-tolerant circuits. Here, we investigate a lattice-surgery-based CNOT operation between two surface code patches under phenomenological error models. (i) For two-qubit logical Pauli measurements – the elementary building block of the CNOT – we optimize the number of stabilizer measurement rounds, usually taken equal to d, the size (code distance) of each patch. We find that the optimal number can be greater or smaller than d, depending on the rate of physical and readout errors, and the separation between the code patches. (ii) We fully characterize the two-qubit logical error channel of the lattice-surgery-based CNOT. We find a symmetry of the CNOT protocol, that results in a symmetry of the logical error channel. We also find that correlations between X and Z errors on the logical level are suppressed under minimum weight decoding.

A Trustworthy and Responsible Decision-Making Framework for Resource Management in Food–Energy–Water Nexus: A Control-Theoretical Approach

This article introduces a hybrid framework for trustworthy and responsible natural resource management, aimed at building bottom-up trust to enhance cooperation among decision-makers in the food, energy, and water sectors. Cooperation is highly critical for the adoption and application of resource management alternatives (solutions), including those generated by AI-based recommender systems, in communities due to significant impact of these sectors on the environment and the economic productivity of affected communities. While algorithms can recommend solutions, effectively communicating and gaining community acceptance of these solutions is crucial. Our research stands out by emphasizing the collaboration between humans and machines, which is essential for addressing broader challenges related to climate change and the need for expert tradeoff handling in the management of natural resources. To support future decision-making, we propose a successful control-theory model based on previous decision-making and actor behavior. We utilize control theory to depict how community decisions can be affected by how much individuals trust and accept proposed solutions on irrigation water rights and crop operations in an iterative and interactive decision support environment. This model interacts with stakeholders to collect their feedback on the acceptability of solutions, while also examining the influence of consensus levels, trust sensitivities, and the number of decision-making rounds on the acceptance of proposed solutions. Furthermore, we investigate a system of multiple decision-making and explore the impact of learning actors who adjust their trust sensitivities based on solution acceptance and the number of decision-making rounds. Additionally, our approach can be employed to evaluate and refine potential policy modifications. Although we assess potential outcomes using hypothetical actions by individuals, it is essential to emphasize our primary objective of developing a tool that accurately captures real human behavior and fosters improved collaboration in community decision-making. Ultimately, our aim is to enhance the harmony between AI-based recommender systems and human values, promoting a deeper understanding and integration between the two.

A Two-Stage Approach for Secure Node Localization and Optimal Route Selection for Enhanced Performance in Wireless Sensor Networks

In Wireless Sensor Network (WSN), node localization is a crucial need for precise data gathering and effective communication. However, high energy requirements, long inter-node distances and unpredictable limitations create problems for traditional localization techniques. This study proposes an innovative two-stage approach to improve localization accuracy and maximize route selection in WSNs. In the first stage, the Self-Adaptive Binary Waterwheel Plant Optimization (SA-BWP) algorithm is used to evaluate a node’s trustworthiness to achieve accurate localization. In the second stage, the Gazelle-Enhanced Binary Waterwheel Plant Optimization (G-BWP) method is employed to determine the most effective data transfer path between sensor nodes and the sink. To create effective routes, the G-BWP algorithm takes into account variables like energy consumption, shortest distance, delay and trust. The goal of the proposed approach is to optimize WSN performance through precise localization and effective routing. MATLAB is used for both implementation and evaluation of the model, which shows improved performance over current methods in terms of throughput, delivery ratio, network lifetime, energy efficiency, delay reduction and localization accuracy in terms of various number of nodes and rounds. The proposed model achieves highest delivery ratio of 0.97, less delay of 5.39, less energy of 23.3 across various nodes and rounds.

Epidemiology of Rounding Error.

This work represents a significant contribution to understanding the importance of appropriately rounding numbers with minimal error. That is, to reduce inexact rounding and data truncation error and simultaneously eliminate unintentional misleading findings in epidemiological studies. The rounding of numbers represents a compromise solution that attempts to find a balance between the loss of information from reporting too few significant digits versus retaining more digits than necessary. Substituting a rounded number for its original value may be acceptable and practical in many applied situations if an adequate degree of accuracy is retained. On the other hand, numeric error may result from improper rounding or data truncation which, in effect, compromises the credibility of study findings and may lead to a false sense of discovery. Performing complex computations on such values, especially when sequential or composite operations are involved, can lead to error propagation and inaccurate results. Having an overall awareness of the nature and impact of rounding error, including preventive actions, can contribute greatly to the integrity of research, yielding more reliable and accurate conclusions. Heuristic examples are provided to illustrate the consequences of rounding and data truncation error in epidemiology studies, specifically those pertaining to relative effect estimation.

Opening the Blackbox: Collision Attacks on Round-Reduced Tip5, Tip4, Tip4’ and Monolith

A new design strategy for ZK-friendly hash functions has emerged since the proposal of Reinforced Concrete at CCS 2022, which is based on the hybrid use of two types of nonlinear transforms: the composition of some small-scale lookup tables (e.g., 7-bit or 8-bit permutations) and simple power maps over Fp. Following such a design strategy, some new ZK-friendly hash functions have been recently proposed, e.g., Tip5, Tip4, Tip4’, and the Monolith family. All these hash functions have a small number of rounds, i.e., 5 rounds for Tip5, Tip4, and Tip4’, and 6 rounds for Monolith (recently published at ToSC 2024/3). Using the composition of some small-scale lookup tables to build a large-scale permutation over Fp – which we call S-box – is a main feature in such designs, which can somehow enhance the resistance against the Gröbner basis attack because this large-scale permutation will correspond to a complex and high-degree polynomial representation over Fp.As the first technical contribution, we propose a novel and efficient algorithm to study the differential property of this S-box and to find a conforming input pair for a randomly given input and output difference. For comparison, a trivial method based on the use of the differential distribution table (DDT) for solving this problem will require time complexity O(p2).For the second contribution, we also propose new frameworks to devise efficient collision attacks on such hash functions. Based on the differential properties of these S-boxes and the new attack frameworks, we propose the first collision attacks on 3-round Tip5, Tip4, and Tip4’, as well as 2-round Monolith-31 and Monolith-64, where the 2-round attacks on Monolith are practical. In the semi-free-start (SFS) collision attack setting, we achieve practical SFS collision attacks on 3-round Tip5, Tip4, and Tip4’. Moreover, the SFS collision attacks can reach up to 4-round Tip4 and 3-round Monolith-64. As far as we know, this is the first third-party cryptanalysis of these hash functions, which improves the initial analysis given by the designers.

A New Practical Cube Attack via Recovering Numerous Superpolys

Cube attack is one of the most powerful approaches for recovering keys of stream ciphers. Practical cube attacks generate several superpolys first and solve the system constructed by these superpolys afterward. Unlike previous practical attacks, we propose a new cube attack that transfers the difficulty of generating easy-solving superpolys to solving the system built by numerous nonlinear ones. In the offline phase, we recovered lots of nonlinear superpolys by improving the approach proposed by Delaune et al. at SAC 2022 in theory. In the online phase, taking advantage of the sparsity and asymmetry of these numerous superpolys, we present a new testing method to solve the constructed system efficiently. As applications, the latest attack could practically recover the keys for 820- and 832-round Trivium with the time complexity no more extensive than 246 and 250, while the previous highest number of rounds of Trivium that can be attacked practically is 830. We believe the proposed approach can be used to attack more rounds of Trivium and other stream ciphers.

Improved integral neural distinguisher model for lightweight cipher PRESENT

PRESENT is a lightweight block cipher, which has attracted many scholars to research its security. In 2022, Zahednejad et al. proposed the integral neural distinguisher on round-reduced PRESENT. In this paper, a new integral neural distinguisher for PRESENT is constructed. In contrast to Zahednejad’s works, the proposed integral neural distinguisher can improve the number of attack rounds in one round. This paper proposes a new data format (invP0n,invP1n,…,invP15n,invS0n,invS1n,…,invS15n), which can exposes more features of PRESENT previous round ciphertext. Simultaneously, this paper incorporates MBConv module into the convolutional layers of DenseNet, which enable the neural network to identify a greater variety of features in ciphertext. The data format of the paper is analysed. The results of the analysis show that the data format in this paper is able to identify more features compared to Zahednejad’s data format. Further to this, experiments are performed on PRESENT using the integral neural distinguisher in this paper. The experimental results show that the changes make to the neural network and data format have improved the accuracy of distinguishers. Finally, key recovery attacks are conducted on the integral neural distinguishers of SmallPresent-(8) to demonstrate the efficacy of the distinguisher proposed in this paper. The results demonstrate that the key recovery success rates for 5-round and 6-round are 98% and 90%, considering error bits within a range of two bits.

The effects of modest intake of soy milk enriched with Lactobacillus casei and omega-3 on the testis parameters in diabetic rats: a stereological study.

Diabetes mellitus is a widely distributed endocrine disorder in the world. Altered reproductive function is a notable long-term consequence of type 1 diabetes mellitus (T1DM). In the current study, we assessed the effects of soya milk containing Lactobacillus casei and omega-3 on stereology of testes in type 1 diabetic rats. 30 male Sprague Dawley rats were randomly allocated into five groups. Streptozocin (STZ (60 mg/kg)) was applied for diabetes induction. The non-diabetic and diabetic control groups were fed with 1 ml of distilled water. Three treatment diabetic groups were fed 1 ml of Soy milk group (SM), Probiotic soy milk group (PSM), and Omega-3 probiotic soy milk group (OPSM) via intragastric gavage for 60 days. At the endpoint, the animals were sacrificed and serum luteinizing hormone (LH), Follicle-stimulating hormone (FSH), testosterone, MDA besides testicular, and seminal parameters were analyzed. The administration of soy milk supplemented with L. casei and omega-3 in diabetic rats elevated the concentrations of LH, FSH, testosterone, and reduced malondialdehyde (MDA). In addition, this combination improved sperm quality, enhanced the number of sperm with rapid progress, increased testis weight and volume, seminiferous tubule and germinal epithelium volume; and also augmented the number of spermatogonia, spermatocyte, round and long spermatids, Sertoli cells and Leydig cells. Supplementation with soy milk containing L. casei and omega-3 can inhibit T1DM-induced infertility rats through improving testicular parameters, enhancing sperm quality, and increasing Sertoli and Leydig cell number.

Approach for participatory Delphi method for structuring voices, population empowerment and research training

A Delphi panel is a methodology used to generate consensus among experts. The development of a participatory Delphi approach consists of including the research population in the design and conduct of the research. It involves panelists in the preparation of an open-ended questionnaire as well as in the responding process. The answers are organized, anonymized and redistributed to produce a synthesis, which becomes a potential consensus statement. This process takes place during a set number of rounds in which the synthesis of each question is analyzed and graded by the panelists. Finally, syntheses are classified as having a higher, medium or lower consensus. We created a ‘change-oriented’ Delphi panel in which health-related students are considered the expert population. In this study, we propose a methodology to collect and analyze health-related students' views on education, demonstrating the effectiveness of active student participation and the Delphi approach in designing educational curricula.•This research focuses on how to conduct a Delphi participatory panel in which health-related students contribute to the study design, data collection, and analysis, as well as serving as the expert panelists.•This highly replicable methodology could be applied in the development of education curricula.•Delphi participatory approaches offer a research introduction opportunity.

Performance Comparison of UCB, TS, and -Greedy TS Algorithms through Simulation of Multi-Armed Bandit Machine

Multi-Armed Bandit (MAB) algorithms are classic algorithms that address sequential decision-making under uncertainty by solving the exploration-exploitation trade-off dilemma. This study investigates the performance comparison of multiple MAB algorithms using a simulated multi-armed bandit machine with Bernoulli reward distribution as the experimental environment. This study compares the performance differences among Upper Confidence Bound (UCB), Thompson Sampling (TS) and -Greedy Thompson Sampling (-TS) in this environment, and attempts to set different parameters, namely the number of arms and the number of experimental rounds and calculate the corresponding cumulative regret of the algorithm under various conditions. The size of the cumulative regret reflects the performance of each algorithm in the simulated slot machine model with rewards that conform to the Bernoulli distribution. In addition, the algorithm running time under the same conditions is also recorded to analyze from the perspective of algorithm efficiency. The experimental results show that under the experimental environment of this study, the cumulative regret produced by the UCB algorithm is more than three times that of the other two algorithms. When the number of trials is small, the cumulative regret generated by the TS algorithm is small, but overall, the performance of the TS algorithm and the -TS algorithm set in this experiment in minimizing the cumulative regret is not much different. However, TS runs in a shorter time under the same conditions. The results of this experiment show that after the number of rounds of experimental operation reaches a large enough number, the operating efficiency of the TS algorithm will be significantly higher than that of the -TS algorithm. TS algorithms have higher randomness, so they show better performance under this experimental condition. The -TS under the parameter setting of this study encourages exploration more in the early stage of the experiment, so it will produce greater cumulative regret than the traditional TS algorithm. In the long run, the performance difference between the two in the multi-armed bandit problem with Bernoulli distribution of rewards is very small. However, the TS algorithm has more advantages in algorithm execution efficiency, so when solving similar problems, the TS algorithm is a better choice.

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.