Analysis of system trustworthiness based on information flow noninterference theory

The trustworthiness analysis and evaluation are the bases of the trust chain transfer. In this paper the formal method of trustworthiness analysis of a system based on the noninterference(NI) theory of the information flow is studied. Firstly, existing methods cannot analyze the impact of the system states on the trustworthiness of software during the process of trust chain transfer. To solve this problem, the impact of the system state on trustworthiness of software is investigated, the run-time mutual interference behavior of software entities is described and an interference model of the access control automaton of a system is established.Secondly, based on the intransitive noninterference(INI) theory, a formal analytic method of trustworthiness for trust chain transfer is proposed, providing a theoretical basis for the analysis of dynamic trustworthiness of software during the trust chain transfer process.Thirdly, a prototype system with dynamic trustworthiness on a platform with dual core architecture is constructed and a verification algorithm of the system trustworthiness is provided. Finally, the monitor hypothesis is extended to the dynamic monitor hypothesis, a theorem of static judgment rule of system trustworthiness is provided, which is useful to prove dynamic trustworthiness of a system at the beginning of system construction. Compared with previous work in this field, this research proposes not only a formal analytic method for the determination of system trustworthiness,but also a modeling method and an analysis algorithm that are feasible for practical implementation.

Component-Based Software Trustworthiness Fuzzy Analytic Hierarchy Process Model

For a more accurate and comprehensive assessment of the trustworthiness of component-based software system,the fuzzy analytic hierarchy process is introduced to establish the analysis model. Combine qualitative and quantitative analyses,the impacts to overall trustworthiness by the different types of components are distinguished. Considering the coupling relationship between components,dividing the system into several layers from target layer to scheme layer,evaluating the scheme advantages disadvantages by group decision-making,the trustworthiness of a typical J2EE structured component-based software is assessed. The trustworthiness asses model of the software components provides an effective methods of operation.

Data trustworthiness and user reputation as indicators of VGI quality

Volunteered geographic information(VGI)has entered a phase where there are both a substantial amount of crowdsourced information available and a big interest in using it by organizations.But the issue of deciding the quality of VGI without resorting to a comparison with authoritative data remains an open challenge.This article first formulates the problem of quality assessment of VGI data.Then presents a model to measure trustworthiness of information and reputation of contributors by analyzing geometric,qualitative,and semantic aspects of edits over time.An implementation of the model is running on a small data-set for a preliminary empirical validation.The results indicate that the computed trustworthiness provides a valid approximation of VGI quality.

Adversarial Attack-Based Robustness Evaluation for Trustworthy AI

Artificial Intelligence(AI)technology has been extensively researched in various fields,including the field of malware detection.AI models must be trustworthy to introduce AI systems into critical decisionmaking and resource protection roles.The problem of robustness to adversarial attacks is a significant barrier to trustworthy AI.Although various adversarial attack and defense methods are actively being studied,there is a lack of research on robustness evaluation metrics that serve as standards for determining whether AI models are safe and reliable against adversarial attacks.An AI model’s robustness level cannot be evaluated by traditional evaluation indicators such as accuracy and recall.Additional evaluation indicators are necessary to evaluate the robustness of AI models against adversarial attacks.In this paper,a Sophisticated Adversarial Robustness Score(SARS)is proposed for AI model robustness evaluation.SARS uses various factors in addition to the ratio of perturbated features and the size of perturbation to evaluate robustness accurately in the evaluation process.This evaluation indicator reflects aspects that are difficult to evaluate using traditional evaluation indicators.Moreover,the level of robustness can be evaluated by considering the difficulty of generating adversarial samples through adversarial attacks.This paper proposed using SARS,calculated based on adversarial attacks,to identify data groups with robustness vulnerability and improve robustness through adversarial training.Through SARS,it is possible to evaluate the level of robustness,which can help developers identify areas for improvement.To validate the proposed method,experiments were conducted using a malware dataset.Through adversarial training,it was confirmed that SARS increased by 70.59%,and the recall reduction rate improved by 64.96%.Through SARS,it is possible to evaluate whether an AI model is vulnerable to adversarial attacks and to identify vulnerable data types.In addition,it is expected that improved models can be achieved by improving resistance to adversarial attacks via methods such as adversarial training.

A new type of blockchain for secure message exchange in VANET

In the Vehicular Ad-hoc NETworks(VANET),the collection and dissemination of life-threatening traffic event information by vehicles are of utmost importance.However,traditional VANETs face several security issues.We propose a new type of blockchain to resolve critical message dissemination issues in the VANET.We create a local blockchain for real-world event message exchange among vehicles within the boundary of a country,which is a new type of blockchain suitable for the VANET.We present a public blockchain that stores the node trustworthiness and message trustworthiness in a distributed ledger that is appropriate for secure message dissemination.

Screening for scoliosis-New recommendations,old dilemmas,no straight solutions

This opinion review considers the prevailing question of whether to screen or notto screen for adolescent idiopathic scoliosis. New and improved standards ofpeople-oriented care and person-centredness, as well as improved principles ofpreventive screening and guideline development, have been postulated andimplemented in health care systems and cultures. Recommendations addressingscreening for scoliosis differ substantially, in terms of their content, standards ofdevelopment and screening principles. Some countries have discontinued issuingrecommendations. In the last decade, a number of updated and newrecommendations and statements have been released. Systematically developedguidelines and recommendations are confronted by consensus and opinion-basedstatements. The dilemmas and discrepancies prevail. The arguments concentrateon the issues of the need for early detection through screening in terms of theeffectiveness of early treatment, on costs and cost-effectiveness issues, scientificand epidemiologic value of screenings, and the credibility of the sources ofevidence. The problem matter is of global scale and applies to millions of people.It regards clinical and methodological dilemmas, but also the matter of vulnerableand fragile time of adolescence and, more generally, children’s rights. Thedecisions need to integrate people’s values and preferences – screening tests needto be acceptable to the population, and treatments need to be acceptable forpatients. Therefore we present one more crucial, but underrepresented in thediscussion, issue of understanding and implementation of the contemporaryprinciples of person-centred care, standards of preventive screening, andguideline development, in the context of screening for scoliosis.

Personal Data Use and Morality in the E-Business Environment

This paper deals with personal data use by firms in the e-business environment from the viewpoint of business administration and information ethics. Whereas the tremendous development of information and communication technology (ICT) has made it easier for firms to acquire, store, share, and utilise personal data on their customers, firms that use personal data are exposed to risks related to privacy issues. Since individuals fear the invasion of their privacy, the failure of a firm to appear or remain trustworthy would make it difficult for it to maintain accurate, up-to-date databases and to construct desirable business processes, which would affect the bottom line. Therefore, modern firms should do what they can to ensure that their customers trust them. For them, one promising way to remain trustworthy is to behave as a moral agent. Although it is difficult for any firm to meet the conditions necessary to be a moral agent, competence in behaving as a moral agent is a hard-to-imitate capability af firms for which personal data use is vital for enjoying the benefits of business relationships in the e-business environment.

Research and Analysis of Structural Hole and Matching Coefficient

Measure is a map from the reality or experimental world to the mathematical world, through which people can more easily understand the properties of entities and the relationship between them. But the traditional software measurement methods have been unable to effectively measure this large-scale software. Therefore, trustworthy measurement gives an accurate measurement to these emerging features, providing valuable perspectives and different research dimensions to understand software systems. The paper introduces the complex network theory to software measurement methods and proposes a statistical measurement methodology. First we study the basic parameters of the complex network, and then introduce two new measurement parameters: structural holes, matching coefficient.

Security,Controllability,Manageability and Survivability in Trustworthy Network

The Internet plays increasingly important roles in everyone's life; however, the existence of a mismatch between the basic architectural idea beneath the Internet and the emerging requirements for it is becoming more and more obvious. Although the Internet community came up with a consensus that the future network should be trustworthy, the concept of "trustworthy networks" and the ways leading us to a trustworthy network are not yet clear. This research insists that the security, controllability, manageability, and survivability should be basic properties of a trustworthy network. The key ideas and techniques involved in these properties are studied, and recent developments and progresses are surveyed. At the same time, the technical trends and challenges are briefly discussed. The network trustworthiness could and should be eventually achieved.

Quality of Chinese Export Toys Is Trustworthy

On September 4th, 2007, AQSIQ and the Press Office of the State Council invited 13 media representatives from countries such as America, Britain, France, Japan, Canada, Singapore to visit the Technical Center Toy Laboratory of Guangdong Entry-exit Inspection and Quarantine Bureau, Zhentai (China) Industrial Limited Company and Guangdong Xinboxing Toys Limited Company.……

Uncertainty-Aware Deep Learning: A Promising Tool for Trustworthy Fault Diagnosis

Recently,intelligent fault diagnosis based on deep learning has been extensively investigated,exhibiting state-of-the-art performance.However,the deep learning model is often not truly trusted by users due to the lack of interpretability of“black box”,which limits its deployment in safety-critical applications.A trusted fault diagnosis system requires that the faults can be accurately diagnosed in most cases,and the human in the deci-sion-making loop can be found to deal with the abnormal situa-tion when the models fail.In this paper,we explore a simplified method for quantifying both aleatoric and epistemic uncertainty in deterministic networks,called SAEU.In SAEU,Multivariate Gaussian distribution is employed in the deep architecture to compensate for the shortcomings of complexity and applicability of Bayesian neural networks.Based on the SAEU,we propose a unified uncertainty-aware deep learning framework(UU-DLF)to realize the grand vision of trustworthy fault diagnosis.Moreover,our UU-DLF effectively embodies the idea of“humans in the loop”,which not only allows for manual intervention in abnor-mal situations of diagnostic models,but also makes correspond-ing improvements on existing models based on traceability analy-sis.Finally,two experiments conducted on the gearbox and aero-engine bevel gears are used to demonstrate the effectiveness of UU-DLF and explore the effective reasons behind.

Provenance Documentation to Enable Explainable and Trustworthy AI:A Literature Review

Recently artificial intelligence(AI)and machine learning(ML)models have demonstrated remarkable progress with applications developed in various domains.It is also increasingly discussed that AI and ML models and applications should be transparent,explainable,and trustworthy.Accordingly,the field of Explainable AI(XAI)is expanding rapidly.XAI holds substantial promise for improving trust and transparency in AI-based systems by explaining how complex models such as the deep neural network(DNN)produces their outcomes.Moreover,many researchers and practitioners consider that using provenance to explain these complex models will help improve transparency in AI-based systems.In this paper,we conduct a systematic literature review of provenance,XAI,and trustworthy AI(TAI)to explain the fundamental concepts and illustrate the potential of using provenance as a medium to help accomplish explainability in AI-based systems.Moreover,we also discuss the patterns of recent developments in this area and offer a vision for research in the near future.We hope this literature review will serve as a starting point for scholars and practitioners interested in learning about essential components of provenance,XAI,and TAI.

Enabling affordances of blockchain in agri-food supply chains:A value-driver framework using Q-methodology

The application of blockchain beyond cryptocurrencies has received increasing attention from industry and scholars alike.Given predicted looming food crises,some of the most impactful deployments of blockchains are likely to concern food supply chains.This study outlined how blockchain adoption can result in positive affordances in the food supply chain.Using Q-methodology,this study explored the current status of the agri-food supply chain and how blockchain technology could be useful in addressing existing challenges.This theorization leads to the proposition of the 3TIC value-driver framework for determining the enabling affordances of blockchain that would increase shared value for stakeholders.First,we propose a framework based on the most promising features of blockchain technology to overcome current challenges in the agri-food industry.Our value-driver framework is driven by the Q-study findings of respondents closely associated with the agri-food supply chain.This framework can provide supply chain stakeholders with a clear perception of blockchain affordances and serve as a guideline for utilizing appropriate features of technology that match organizations’capabilities,core competencies,goals,and limitations.Therefore,it could assist top-level decision-makers in systematically evaluating parts of the organization to focus on and improve the infrastructure for successful blockchain implementation along the agri-food supply chain.We conclude by noting certain significant challenges that must be carefully addressed to successfully adopt blockchain technology.

Trustworthy decentralized collaborative learning for edge intelligence:A survey

Edge intelligence is an emerging technology that enables artificial intelligence on connected systems and devices in close proximity to the data sources.decentralized collaborative learning(DCL)is a novel edge intelligence technique that allows distributed clients to cooperatively train a global learning model without revealing their data.DCL has a wide range of applications in various domains,such as smart city and autonomous driving.However,DCL faces significant challenges in ensuring its trustworthiness,as data isolation and privacy issues make DCL systems vulnerable to adversarial attacks that aim to breach system confidentiality,undermine learning reliability or violate data privacy.Therefore,it is crucial to design DCL in a trustworthy manner,with a focus on security,robustness,and privacy.In this survey,we present a comprehensive review of existing efforts for designing trustworthy DCL systems from the three key aformentioned aspects:security,robustness,and privacy.We analyze the threats that affect the trustworthiness of DCL across different scenarios and assess specific technical solutions for achieving each aspect of trustworthy DCL(TDCL).Finally,we highlight open challenges and future directions for advancing TDCL research and practice.

Preface

As a continuation of last four years’ special sections on software systems, this special section encourages and promotes research to address challenges from the perspective of software systems. The goal of this special section is to present state-of-the-art and high-quality original research in the area of software systems. Different from previous special sections, this special section includes two different major themes: 1) Internetware and Beyond;2) Trustworthy Computing Systems and Networks.

FinB rain 2.0:when finance meets trustworthy AI

Artificial intelligence(AI) has accelerated the advancement of financial services by identifying hidden patterns from data to improve the quality of financial decisions. However, in addition to commonly desired attributes,such as model accuracy, financial services demand trustworthy AI with properties that have not been adequately realized. These properties of trustworthy AI are interpretability, fairness and inclusiveness, robustness and security,and privacy protection. Here, we review the recent progress and limitations of applying AI to various areas of financial services, including risk management, fraud detection, wealth management, personalized services, and regulatory technology. Based on these progress and limitations, we introduce FinBrain 2.0, a research framework toward trustworthy AI. We argue that we are still a long way from having a truly trustworthy AI in financial services and call for the communities of AI and financial industry to join in this effort.

Effects of attention and reliability on the performance of online medical crowdfunding projects: The moderating role of target amount

Medical crowdfunding platform helps numerous patients access enthusiastic donors and address financial difficulties,but many crowdfunding projects fail to reach their target amount.Thus,how a crowdfunding project can attract considerable donors is question-able.This study examines the effects of attention and reliability on the performance of online medical crowdfunding projects and how target amount changes such effects.Based on objective data of 1177 crowdfunding projects from 2016 to January 2018 in a large medical crowdfunding platform in China,we find that the project donor's attention(the number of forwards and comments)and reliability(the number of dynamic updates,empirical users,and pictures)positively affect the medical crowdfunding performance of the projects.However,target amount weakens the positive effects of the number of for-wards and comments in online medical crowdfunding projects.Therefore,project spon-sors should set reasonable target fundraising amounts while showing attention and reliability to donors.Compared with previous research that mainly explores the influence of external factors on crowdfunding outcomes from the perspective of donors,this paper focuses on the internal project factors and explores the concerns and trustworthiness of crowdfunding projects,enriching the literature related to fundraising ability in the project.