Specific Issues Research Articles

الآثار المترتبة على الخلع في مرض الموت: دراسة فقهية قانونية مقارنة

This research deals with the hypothesis of the occurrence of Khul’ in the death illness of one of the spouses, and since it is most likely permissible for a foreigner to donate to pay the compensation for Khul’ to the husband instead of his wife, the research also deals with the hypothesis that Khul’ occurs in the death illness of this foreigner who donated to pay the compensation. In the research, I conducted a comparative study between Islamic jurisprudence and personal status laws, where I addressed the position of each of them regarding the problems of the research. In this research, I have adopted the inductive approach, relying on the Holy Quran, the books of the Noble Prophetic Sunnah, the books of Islamic jurisprudence, as well as personal status laws. Through it, I have concluded that divorce in the event of a terminal illness is valid according to Sharia, whether the husband is the terminally ill patient, or the wife, or even a stranger who guarantees the compensation for divorce. As for the amount of compensation, jurists have differed on it according to whether the wife is the patient, the husband, or the stranger. As for personal status laws, most of them have been silent about all the hypotheses of the issue, relying on referring to a doctrine that is referred to when the law is devoid of a specific issue, or relying on the existence of a text that addresses the actions of the terminally ill patient in general, whether by the same law or civil law.

Design Strategies to Minimize Mobile Usability Issues in Navigation Design Patterns

Recent development in mobile technology has significantly improved the quality of life. Everyday life is increasingly becoming dependent on mobile devices as mobile applications are targeting the needs of the end users. However, many end users struggle with navigating mobile applications, leading to frustration, especially with sophisticated and unfamiliar interfaces. This study focuses on addressing specific usability issues in mobile applications by investigating the impact of introducing a floating action button (FAB) and icons with names at the bottom in popular applications such as YouTube, Plex, and IMDb. The current research includes three studies: Study-1 explores the navigation issues that users face; Study-2 measures the experiences of the users with improved navigation designs; and Study-3 compares the results of Study-1 and Study-2 to evaluate user experience with both existing and improved navigation designs. A total of 147 participants participated and the systems usability scale was used to evaluate the navigation design. The experiments indicated that the existing design patterns are complex and difficult to understand leading to user frustration compared to newly designed and improved navigation designed patterns. Moreover, the proposed newly designed navigation patterns improved the effectiveness, learnability, and usability. Consequently, the results highlight the imperativeness of effective navigation design in improving user satisfaction and lowering frustration with mobile applications.

The last decade of air pollution epidemiology and the challenges of quantitative risk assessment.

Epidemiologic research and quantitative risk assessment play a crucial role in transferring fundamental scientific knowledge to policymakers so they can take action to reduce the burden of ambient air pollution. This commentary addresses several challenges in quantitative risk assessment of air pollution that require close attention. The background to this discussion provides a summary of and conclusions from the epidemiological evidence on ambient air pollution and health outcomes accumulated since the 1990s. We focus on identifying relevant exposure-health outcome pairs, the associated concentration-response functions to be applied in a risk assessment, and several caveats in their application. We propose a structured and comprehensive framework for assessing the evidence levels associated with each exposure-health outcome pair within a health impact assessment context. Specific issues regarding the use of global or regional concentration-response functions, their shape, and the range of applicability are discussed.

Specific road safety issues in LMICs: an overview and some illustrative examples

This paper gives an overview of the work of PIARC Working Group 3.1.1 ‘Specific road safety issues for LMICs’. The working group performed its activities in the period 2019–2023. We prepared two deliverables: (a) a literature review, and (b) an overview of illustrative examples. The review started from the notion that the road safety problem requires a multi-disciplinary approach. The safe system approach is generally considered as the central concept for this purpose and this starting point is also taken in this study. However new directions are needed to make the safe system concept work in LMICs. The period of the first UN Decade of Action for Road Safety has brought us a series of valuable problem analysis and overview articles prepared by a variety of institutions and institutes. As a PIARC working group we did not repeat this effort, but used it as a starting point to indicate which specific issues do require a boost in LMICs for the coming period up to 2030. We concluded that the following 12 issues deserve special attention. Strategical: (i) Sustainable Development Goals integral approach, (ii) safety culture, (iii) road safety management and leadership, (iv) building road safety expertise and science; Tactical: (v) transportation system as a whole, (vi) city design, architecture, land use, rural planning, (vii) cost effectiveness, (viii) legislation and enforcement; Operational: (ix) speed, (x) sustainable safe roads, (xi) vehicles as a safe system component, (xii) post-crash health care. As a result of our discussions and analyses we also concluded that it is worthwhile and necessary to make a distinction between the issues in terms of levels of approach. Making roads and vehicles safe may be considered as operational safety measures on a component level, whereas city design and transport system issues do represent a more tactical approach. On top of that, organisational and cultural aspects do have a more strategical nature. As in many managerial and planning tasks the distinction between strategical, tactical and operational levels of approach is needed in the road safety field. It illustrates that the Safe System concept asks for a multi-level vision, thus preventing a silo approach with a limited focus on human behaviour, safe roads or vehicle safety. A further analysis for each of the selected issues resulted in priority areas, i.e. topics that deserve special attention in the LMIC context. In a similar line of reasoning we also described illustrative examples, i.e. LMIC related before-after studies that show the perspective of certain measures on an evidence basis. Although we found some illustrative cases for each of these issues, our search for evidence based cases confirmed the findings from recent overview articles indicating the lack of LMIC related road safety research. A more widespread implementation of effective road safety measures in LMICs would require a stronger national commitment and leadership from the road safety agencies point of view in the first place. Moreover an evidence based policy asks for a strong national and regional knowledge infrastructure. Ultimately road safety policies and road safety research programs are to be considered as inseparable. A strong local knowledge infrastructure may thus become the basis for an effective national road safety policy.

Problem-based learning on the WeChat public platform in radiation oncology residency training programs.

This study explored the effectiveness of a Problem-based Learning (PBL) teaching model on the WeChat public platform for radiation oncology residency training programs. The WeChat PBL program was initiated in 2019. The study recruited student participants, on a voluntary basis, who were in their first and second years of standardized training in radiation oncology. We selected classic and trending issues in radiation oncology as the two main teaching topics. Students in the PBL group searched for relevant literature, summarizing PowerPoint presentations or academic speeches on specific issues published on the professional WeChat public platform after an in-depth review by teachers. The final national standardized training examination scores for resident physicians were used to evaluate teaching effectiveness. The graduation scores of the PBL and non-PBL groups were compared, and a stratified analysis was conducted based on the year of participation to assess the teaching effectiveness of this model. Moreover, questionnaires were administered to evaluate students' experiences. Among the 36 students who joined the study in 2019, 16 (44.0%) voluntarily joined the PBL teaching group. Nine students started teaching PBL in their first year of residency, accounting for 47.4% of that year, and seven started in their second year, accounting for 41.2%. The average graduation scores of the PBL group were superior to those of the non-PBL-focused group for the same grade. The rate of high scores among students in the PBL group (56.3%) was higher than that of students in the non-PBL group (25%). Specifically, first-year residents who joined the PBL group had an average score of 92.3 ± 2.2, significantly higher than their non-PBL counterparts, who scored an average of 88.6 ± 4.6, showing a statistically significant advantage (p = 0.042). All students were satisfied with the PBL WeChat teaching and would recommend the PBL WeChat course to others. The PBL residency teaching model on the WeChat platform improved residency scores and garnered student satisfaction.

Syngas Production via Oxidative Reforming of Propane Using a CO2- and O2-Permeating Membrane

Recently, ceramic–carbonate membrane reactors have been proposed to selectively separate CO2 at elevated temperatures and to valorize this pollutant gas by coupling a catalyzed reaction. This work explores using a membrane reactor to perform the oxidative reforming of propane by taking advantage of the CO2- and O2-permeating properties of a LiAlO2/Ag–carbonate membrane. The fabricated membrane showed excellent permeation properties, such as CO2/N2 and O2/N2 selectivity, when operating in the 725–850 °C temperature range. The membrane exhibited remarkable stability during the long-term permeation test under operating conditions, exhibiting minor microstructural and permeation changes. Then, by packing a Ni/CeO2 catalyst, the membrane reactor arrangement showed efficient syngas production, especially at temperatures above 800 °C. A hydrogen-rich syngas mixture was obtained by the contributions of the oxidative reforming and cracking reactions. Specific issues observed regarding the membrane reactor’s performance are attributed to the catalyst that was used, which experienced significant poisoning by carbon deposition during the reaction, affecting syngas production during the long-term test. Thermodynamic calculations were performed to support the experimental results.

Framing Pro-Palestine Protests: A Comparative Analysis of News Coverage by The Guardian and USA Today

This study examines the framing strategies employed by The Guardian and USA Today in their coverage of the pro-Palestine protests in American universities during April and May 2024. Accordingly, the corpus comprises 213 news texts, with The Guardian contributing 106 articles and USA Today contributing 107 articles from April 17 to May 17, 2024. The research identifies and contrasts the dominant frames used by these two newspapers by conducting a framing content analysis on the selected news articles. The Guardian frequently utilizes conflict and adversarial frames, emphasizing university-wide ideological and political struggles. Conversely, USA Today focuses on broader political confrontations and administrative accountability, often highlighting institutional responses and governance issues. The analysis process is guided by framing theory to reveal how news representations influence public perception and interpretation and media agenda-setting theory to explore how media news coverage prioritizes specific issues within the same event. The findings underscore the decisive role of media framing in shaping public opinion and priorities, particularly in the context of global and political issues.

Overcoming the Friction between the “Right to be Forgotten” and Blockchain Technology through a New Approach

Objective: this paper explores the challenges arising from the conflict between blockchain technology and the “right to be forgotten” as provided by the European data protection framework.Methods: in the First Section, the author provides a brief description of the evolution of blockchain technology and the most pressing issues between traditional blockchain models and UE’s legislations. Among the latter, the author analyzes the specific issue concerning the clash between the traditional blockchains (both private and public models), typically immutable, and the individual’s right to cancellation or modification of own personal data. This section emphasizes the importance of personal data protection, which has always been one of the main tasks for supranational legislators. The legal regulation of data protection and the relevant judicial practice of the European Court of Human Rights is analyzed. The author raises the problem of expressing the free self-determination of an individual in the form of controlling their personal data on the Internet. The Second Section of this contribution is dedicated to the study of probable ways to solve the existing incompatibility and to make the distributed ledger system compatible with the European data protection legislation. An emphasis is made on the model provided by “Traent” company, which ensures the right to data cancellation or modification. The capability of this model to solve the said contradiction is analyzed.Results: the study delves into the peculiar features of the new model to understand how it strategically utilizes the advantages of public and private blockchains guaranteeing not only the validity and authenticity of the chain where the transaction was performed, but, most importantly, the modification and granular cancellation of client’s personal data. This innovative solution offers a potential path forward for navigating the complex intersection of data privacy and blockchain innovation in the European context.Scientific novelty: Traent has implemented a “hybrid” model blockchain that, incorporating both public and private components, to achieve an effective compliance with the European Union regulations, especially those concerning data protection and privacy.Practical significance: the obtained conclusions and proposals can be taken into consideration in improving the compliance of blockchain technologies with the European Union General Data Protection Regulation.

FORECASTING FIRE RISK VALUES USING MACHINE LEARNING AND TIME SERIES ANALYSIS

The probability of occurrence of emergencies and their consequences can be minimized by predicting events that contribute to the emergence of such a situation. An optimal and useful tool for such prediction and forecasting can be modeling, which can be carried out on the basis of information technology using computer programs.The methods of modeling and forecasting emergencies related to fires are the same as for other emergencies, but there are some specifics. The forecast of emergencies related to fires is aimed at three main stages: 1. forecasting the probability of occurrence with the determination of the place of the highest probability of fire occurrence, time and factors contributing to it; 2. forecasting the development of the situation and the spread of the fire (direction, speed) when it occurs, the process itself; 3. forecasting the consequences of a fire at a specific facility.The forecast is made based on the analysis of all possible unfavorable factors, their fields of action, features of the facility, its fire hazard and other data.The use of machine learning methods and time series analysis for forecasting fire risk values is substantiated. The possibility of using statistical methods of mathematical modeling of fire occurrence as the basis of the machine learning model is noted. This approach enables the machine learning model to process and analyze a large amount of statistical information for specific time periods and issue forecasts.

Les Houches guide to reusable ML models in LHC analyses

With the increasing usage of machine-learning in high-energy physics analyses, the publication of the trained models in a reusable form has become a crucial question for analysis preservation and reuse. The complexity of these models creates practical issues for both reporting them accurately and for ensuring the stability of their behaviours in different environments and over extended timescales. In this note we discuss the current state of affairs, highlighting specific practical issues and focusing on the most promising technical and strategic approaches to ensure trustworthy analysis-preservation. This material originated from discussions in the LHC Reinterpretation Forum and the 2023 PhysTeV workshop at Les Houches.

Ethical implications of artificial intelligence in skin cancer diagnostics: use-case analyses.

Skin cancer is the most common cancer worldwide. Early diagnosis is crucial for improving patient survival and morbidity. Artificial intelligence (AI)-assisted smartphone applications (apps) for skin cancer potentially offer accessible, early risk assessment of suspicious skin lesions. However, the integration of novel technologies into dermatology pathways raises ethical concerns. Although ethical principles for AI governance are well known, how these principles should be applied to real-life AI apps readily available for public use is less well understood. We conducted an ethical use-case analysis of commercially available skin cancer apps to better understand the ethical issues arising from their development and use in a real-world context. Established methods for ethical analysis of clinical AI applications were applied to two popular skin cancer apps in the UK: SkinVision and Scanoma. Systematic searches of published literature, regulatory documents, and websites were conducted to review the evidence regarding app development, effectiveness, and use. Screening for inclusion was undertaken by two researchers independently. Ethical concerns were identified with reference to previously described ethical concerns and principles for AI-assisted healthcare. By conceptualising ethical principles within the use-context of skin cancer apps, we identified specific ethical issues arising throughout the AI lifecycle of both apps. One company provided extensive detail regarding algorithm development and decision-making, this information was insufficiently reported for the other app. Other concerns identified related to number, quality, and consistency of studies assessing algorithm efficacy. Limited efforts to address potential skin tone biases and exclusion of individuals with darker skin tones as target users by one app risks perpetuating existing inequalities. Inadequate regulatory oversight was identified. Findings from our ethical use-case analysis of two patient-facing AI-assisted skin cancer apps suggest inadequate incorporation of bioethical norms such as justice, responsibility and transparency into the development and deployment of both apps. Improved regulation should increase accountability. Ensuring ethics by design through integration between technology developers, dermatologists, ethicists, and the public is urgently needed to prevent the potential benefits of AI-assisted skin cancer apps being overshadowed by potential ethical harms.

Australian private healthcare staff perspectives on patient reported experience measures (PREMs): a qualitative interview study

ObjectivePatient reported experience measures (PREMs) are common tools utilised in hospitals to support quality improvements, allow consumers to provide feedback on care experiences and can be used to support consumers’ hospital selections. This study aimed to understand the views and opinions of private hospital staff on PREM use and the utility of PREMs as a consumer decision-making tool.MethodQualitative, semi-structured interview study conducted via telephone between March-June 2023. Participants (n = 10) were recruited from major private healthcare providers in Australia with half representing hospital-based staff and the other half corporate head office staff who work in patient experience and quality. Interviews were audio-recorded, transcribed, and analysed thematically.ResultsPREM benefits included an understanding of patient experience that improved provision of patient centred care with feedback acting as catalyst for change, to corporate-level strategic initiatives that address specific issues. Drawbacks of PREM reporting included concerns around skewed results by biased respondents, and completion based on hard to alter items (e.g., infrastructure) or on matters outside of hospital control (e.g., insurance). Staff had mixed reactions to consumers using PREMs results when selecting a hospital, some advocated for transparency while others feared consumers would misinterpret the data.ConclusionsImproved real-time reporting of PREMs, learning from other industries about recording customer experience, and mandatory reporting by private hospitals could further the benefits of PREM measurement in private healthcare. Recognised was the need for PREMs to be displayed in a readily understood way so those with limited health literacy can correctly interpret.

Artificial Intelligence Integration in Teacher Education: Navigating Benefits, Challenges, and Transformative Pedagogy

This article explores the potential uses, benefits, and challenges of artificial intelligence (AI) tools for teacher educators and their teacher candidates. It begins with a brief introduction to the topic, followed by a discussion of existing literature concerning the impact of AI on K–12 education; the importance of preparing AI-literate teachers; and specific issues related to AI’s use in teacher education, which includes studies on perceptions about AI utilization in education. The author also examines the challenges of AI implementation in the preparation of teachers; poses critical questions and ethical, pedagogical, and philosophical concerns faculty and students must consider; and provides ideas on using AI tools in courses through exemplar activities. The various activities illustrate the integration of AI- and non-AI-based activities to promote AI literacy among teachers. The goal of these activities is to support candidates’ competency development to become effective teachers while understanding the potential of AI tools for assisting in planning instruction and creating curricula. The exemplars also focus on developing critical thinking, creativity, and collaboration in carrying out pedagogical tasks. The author advocates for embracing AI tools in education cautiously and strategically by recognizing their power to transform teaching and learning for teachers and students.

Democracy Ancient and Modern?

Abstract The idea that democracy is in crisis is nothing new; ancient Greek commentators like Plato and Thucydides argued that political instability was baked into democracy as a regime type. Can examples drawn from classical Athens make a useful contribution to discussions of the failings of contemporary democracy? This article considers a range of interventions from the past decade in the United States and the United Kingdom, carried out at different scales and through different methods. It considers the strengths of working with material from the distant past – its non-partisan nature offering a safe space to discuss political conflict without engaging in it – and also the weaknesses, such as the limited franchise of ancient democracies like classical Athens, and the need to consider specific historical issues in their own context. It also considers the unfamiliarity of ancient history and the way in which knowledge of the classical past can itself be perceived as class-bound and exclusionary.

Large Eddy Simulation of large-scale hydrogen deflagrations using the Thickened Flame Model with stretch sensitivity adaptation and thermo-diffusive instability modeling

Due to the high costs associated to experimental testing of hydrogen (H2) explosions, numerical simulations play an essential role in the design of safe industrial systems. In this work, we investigate the use of the Thickened Flame Model (TFM) to perform simulations of H2 deflagrations. Specific issues arise when considering hydrogen: (i) due to differential diffusion, the flame is very sensitive to stretch; (ii) instabilities of the flame front are generated and lead to an increased flame propagation speed. Both of these phenomena are affected by flame thickening. First, the flame sensitivity to stretch is artificially increased by the thickening. The recently developed Ma-TFM model proposes a correction of diffusive fluxes to reach the correct Markstein length and is selected in this work. Secondly, thermo-diffusive and hydrodynamic flame instabilities are also altered by thickening. By computing the dispersion relationship for a hydrogen/air mixture, we show that the instability scales are proportional to the thickening factor F. A simple subgrid scale (SGS) model, based on the computations of laminar unstable 2-D and 3-D flames, is then considered to take into account the effect of instabilities. The Ma-TFM and SGS instability models are applied together for the first time on a semi-industrial vented lean hydrogen deflagration, leading to promising results. In particular, it is found that considering 3-D unstable flames is necessary to accurately predict the experimental pressure peaks.

What about the negatives? An integrated framework for revealing diverse values of nature and its conservation

Abstract Taking into account the perspectives of local stakeholders is essential for just and effective biodiversity conservation. Plural valuation, making visible the diverse values people hold in relation to nature, has emerged as a key approach towards better inclusion of stakeholders' perspectives. So far, plural valuation has mainly focused on positive values, leaving equally important negative values underexposed. To address this, a concrete proposal has been presented to also consider ‘disvalues’. However, accessible frameworks to help practitioners apply this concept are arguably still lacking. To address this gap, we here propose the ‘Integrated Nature Futures Framework’ (I‐NFF), where ‘integrated’ indicates joint consideration of positive and negative values. The I‐NFF draws on the popular Nature Futures Framework (NFF) to develop disvalue thinking in a more accessible form. The I‐NFF considers three perspectives (‘nature’, ‘society’ and ‘culture’), based on which something can be placed on a spectrum from positive (‘for nature’, ‘for society’ and ‘as culture’) to negative (‘against nature’, ‘against society’ and ‘in conflict with culture’), and can be represented as two mirrored triangles. Using empirical data, we illustrate various purposes for which the I‐NFF can be used: to inventory nature‐related topics ‘at play’ in a given context, to depict how people frame their overall relationship with nature or to depict how people frame specific issues. This demonstrates how the I‐NFF helps reveal value pluralism and trade‐offs, capture the reciprocity of human–nature relationships and identify where perspectives differ and share common ground. We close by discussing how the I‐NFF can enhance the inclusion of stakeholder voices in biodiversity conservation, which is indispensable for a more nature‐ and people‐positive future. Read the free Plain Language Summary for this article on the Journal blog.

A Comprehensive Exploration of Hellwig’s Taxonomic Measure of Development and Its Modifications—A Systematic Review of Algorithms and Applications

This paper presents an original and comprehensive investigation into the Taxonomic Measure of Development (TMD), introduced by Hellwig in 1968, enriching both its theoretical foundations and practical applications. It provides an overview of various variants of the Hellwig method, including their extensions and applications, while also exploring recent trends across multiple research domains. Primarily developed as a method for multidimensional analysis, TMD has evolved into a pivotal tool in multi-criteria decision-making. It is widely used for evaluating and ranking alternatives, particularly in the analysis of complex socio-economic phenomena and decision-making scenarios involving multiple criteria. This study systematically reviews the original algorithm and its subsequent extensions and modifications, including adaptations for fuzzy sets, intuitionistic fuzzy sets, and interval-valued fuzzy sets. Furthermore, it explores an integrated multi-criteria approach based on Hellwig’s method and its practical applications across various domains. This paper introduces an original approach by conducting a detailed, step-by-step analysis of the TMD framework. This process-oriented analysis is a novel contribution that sets this study apart from typical reviews based on statistical or bibliometric data. By examining key steps in the TMD framework—such as data collection, criterion weighting, data normalization, ideal value determination, distance calculation, and normalization factor—this paper highlights the method’s versatility in addressing complex, real-world decision-making problems. Although similar to the widely used Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method in its reliance on distance to evaluate alternatives, Hellwig’s approach is unique in focusing exclusively on proximity to an ideal solution, without considering distance from a negative ideal. This distinctive emphasis has led to numerous adaptations and extensions that address specific issues such as criterion dependencies, uncertainty, and rank reversal. The findings underscore the continued relevance of the Hellwig method, its recent extensions, and its growing international recognition.

Unveiling driving disparities between satisfaction and equity of ecosystem services in urbanized areas

Rapid urbanization intensifies the challenges of achieving satisfaction and equity in ecosystem services (ESs). Beginning with a theoretical overview, we identify five pivotal challenges in managing urban ESs, forming the basis for our integrative analysis. Employing a multi-level analytical perspective, our research quantifies ESs satisfaction through supply–demand ratios and evaluates ESs equity via supply–demand Gini coefficient. Simultaneously explored the driving force relationship between these two dimensions. This study uses Guangdong Province, a typical urbanized area in China, as a case study to assess the supply and demand relationships of four regulating ESs: water retention, soil retention, carbon storage, and water purification. Our results highlight the disparity in ES management effectiveness across different scales while the overall assessments might suggest adequate or equitable ESs, localized assessments within cities and regions often uncover specific ESs issues. A pivotal aspect is that single-factor critical to ESs satisfaction or equity dimension may have limited or opposing importance in another, but interactive effects of multiple factors can effectively mitigate these divergent impacts through non-linear synergies. These insights not only reveal the multiplex impacts of natural–social–economic drivers on urban expansion but also guide the targeted development in different management scale, multi-dimensional management strategies to address these disparities. This approach enriches the discourse on sustainable urban development and provides a robust framework for addressing the nuanced challenges of urban ESs management.

Adversarial contrastive representation training with external knowledge injection for zero-shot stance detection

Zero-shot stance detection (ZSSD) is a task that involves identifying the author’s perspective on specific issues in text, particularly when the target topic has not been encountered during the model training process, to address rapidly evolving topics on social media. This paper introduces a ZSSD framework named KEL-CA. To enable the model to more effectively utilize transferable stance features for representing unseen targets, the framework incorporates a multi-layer contrastive learning and adversarial domain transfer module. Unlike traditional contrastive or adversarial learning, our framework captures both correlations and distinctions between invariant and specific features, as well as between different stance labels, and enhances the generalisation ability and robustness of the features. Subsequently, to address the problem of insufficient information about the target context, we designed a dual external knowledge injection module that uses a large language model (LLM) to extract external knowledge from a Wikipedia-based local knowledge base and a Chain-of-Thought (COT) process to ensure the timeliness and relevance of the knowledge to infer the stances of unseen targets. Experimental results demonstrate that our approach outperforms existing models on two benchmark datasets, thereby validating its efficacy in ZSSD tasks.

Secure fuzzy retrieval protocol for multiple datasets

With the diversification of data sources and the massive growth of datasets, data retrieval has become increasingly complex and time-consuming. In the traditional retrieval method, if a user wants to query multiple datasets, the general approach is to retrieve them one by one in order, which may lead to duplication of work and waste of resources. Private set intersection is a specific issue in secure multi-party computation. It allows several participants, each holding different sets, to jointly calculate the intersection of their sets without revealing any information other than the intersection. This method is naturally suitable for data fusion. In this work, we propose a secure fuzzy retrieval protocol for multiple datasets. First, we use private set intersection technology to fuse multiple datasets. Then, we perform secure retrieval based on this fused dataset, effectively avoiding the waste of resources caused by separate retrievals, thereby maximizing resource efficiency. It is worth mentioning that the protocol proposed in this paper can also be used for fuzzy retrieval to improve the user’s search experience. More importantly, the protocol can maximize privacy protection during the retrieval process, including strict protection of sensitive information such as retrieval keywords, ensuring that user data and query intentions will not be leaked during the entire retrieval process. Finally, we provide a rigorous security proof and demonstrate the effectiveness of the protocol through simulation experiments.