Pub Date : 2024-04-10DOI: 10.60087/jaigs.vol03.issue01.p178
Sohel Rana
Artificial intelligence (AI) has become ubiquitous across various industries, including security, healthcare, finance, and national defense. However, alongside its transformative potential, there has been a concerning rise in malicious exploitation of AI capabilities. Simultaneously, the rapid advancement of cloud computing technology has led to the emergence of cloud-based AI systems. Unfortunately, vulnerabilities inherent in cloud infrastructure also pose security risks to AI services. We recognize the critical role of maintaining the integrity of training data, as any compromise therein directly impacts the effectiveness of AI systems. In response to this challenge, we emphasize the paramount importance of preserving data integrity within AI systems. To address this need, we propose a data integrity architecture guided by the National Institute of Standards and Technology (NIST) cybersecurity framework. Leveraging blockchain technology and smart contracts presents a suitable solution for addressing integrity challenges, given their features of shared and decentralized ledgers. Smart contracts enable automated policy enforcement, facilitate continuous monitoring of data integrity, and help mitigate the risk of data tampering.
{"title":"Ensuring Compliance Integrity in AI ML Cloud Environments: The Role of Data Guardianship","authors":"Sohel Rana","doi":"10.60087/jaigs.vol03.issue01.p178","DOIUrl":"https://doi.org/10.60087/jaigs.vol03.issue01.p178","url":null,"abstract":"Artificial intelligence (AI) has become ubiquitous across various industries, including security, healthcare, finance, and national defense. However, alongside its transformative potential, there has been a concerning rise in malicious exploitation of AI capabilities. Simultaneously, the rapid advancement of cloud computing technology has led to the emergence of cloud-based AI systems. Unfortunately, vulnerabilities inherent in cloud infrastructure also pose security risks to AI services. We recognize the critical role of maintaining the integrity of training data, as any compromise therein directly impacts the effectiveness of AI systems. In response to this challenge, we emphasize the paramount importance of preserving data integrity within AI systems. To address this need, we propose a data integrity architecture guided by the National Institute of Standards and Technology (NIST) cybersecurity framework. Leveraging blockchain technology and smart contracts presents a suitable solution for addressing integrity challenges, given their features of shared and decentralized ledgers. Smart contracts enable automated policy enforcement, facilitate continuous monitoring of data integrity, and help mitigate the risk of data tampering.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140717233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-10DOI: 10.60087/jaigs.vol03.issue01.p195
Md.mafiqul Islam
Cloud computing revolutionizes service provision by delivering virtualized services via the internet. The Cloud, a ubiquitous term for this online space, is managed by service providers. However, users engaging with cloud services face concerns regarding data security and privacy, fearing potential misuse by service providers, who may inadvertently expose sensitive data to unauthorized parties. To address this challenge, we introduce a novel framework called the Cloud Information Accountability (CIA) framework, centered on the concept of data liability. Our framework outlines essential requirements and offers guidelines for achieving robust data accountability in cloud environments. Upon data submission by the owner, the service provider gains full access and control over the data, typically governed by traditional access control mechanisms. To enhance transparency and accountability, we propose an algorithm that automates data access logging via JAR files, providing detailed insights into data usage. Our approach aims to bolster trust, mitigate privacy concerns, and fortify security in cloud computing ecosystems.
云计算通过互联网提供虚拟化服务，彻底改变了服务的提供方式。云 "是这一在线空间的通用术语，由服务提供商管理。然而，使用云服务的用户面临着数据安全和隐私方面的担忧，他们担心服务提供商可能会滥用服务，无意中将敏感数据暴露给未经授权的各方。为了应对这一挑战，我们引入了一个以数据责任概念为核心的新型框架，即云信息责任（CIA）框架。我们的框架概述了基本要求，并为在云环境中实现强大的数据责任提供了指导。在所有者提交数据后，服务提供商就获得了对数据的完全访问权和控制权，这通常受传统访问控制机制的制约。为了提高透明度和责任感，我们提出了一种算法，通过 JAR 文件自动记录数据访问情况，从而详细了解数据的使用情况。我们的方法旨在增强云计算生态系统中的信任、减少隐私问题并加强安全性。
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Pub Date : 2024-04-10DOI: 10.60087/jaigs.vol03.issue01.p160
Sohana Akter
The significance of ensuring security and upholding data privacy within cloud-based workflows is widely recognized in research domains. This importance is particularly evident in contexts such as safeguarding patients' private data managed within cloud-deployed workflows, where maintaining confidentiality is paramount, alongside ensuring secure communication among involved stakeholders. In response to these imperatives, our paper presents an architecture and formal model designed to enforce security measures within cloud workflow orchestration. Central to our proposed architecture is the emphasis on continuous monitoring of cloud resources, workflow tasks, and data streams to detect and preempt anomalies in workflow orchestration processes. To accomplish this, we advocate for a multi-modal approach that integrates deep learning, one-class classification, and clustering techniques. In essence, our proposed architecture offers a comprehensive solution for enforcing security within cloud workflow orchestration, harnessing advanced methodologies like deep learning for anomaly detection and prediction. This approach is particularly pertinent in critical sectors such as healthcare, especially during unprecedented events like the COVID-19 pandemic.
{"title":"Harmonizing Compliance: Coordinating Automated Verification Processes within Cloud-based AI/ML Workflows","authors":"Sohana Akter","doi":"10.60087/jaigs.vol03.issue01.p160","DOIUrl":"https://doi.org/10.60087/jaigs.vol03.issue01.p160","url":null,"abstract":"The significance of ensuring security and upholding data privacy within cloud-based workflows is widely recognized in research domains. This importance is particularly evident in contexts such as safeguarding patients' private data managed within cloud-deployed workflows, where maintaining confidentiality is paramount, alongside ensuring secure communication among involved stakeholders. In response to these imperatives, our paper presents an architecture and formal model designed to enforce security measures within cloud workflow orchestration. Central to our proposed architecture is the emphasis on continuous monitoring of cloud resources, workflow tasks, and data streams to detect and preempt anomalies in workflow orchestration processes. To accomplish this, we advocate for a multi-modal approach that integrates deep learning, one-class classification, and clustering techniques. In essence, our proposed architecture offers a comprehensive solution for enforcing security within cloud workflow orchestration, harnessing advanced methodologies like deep learning for anomaly detection and prediction. This approach is particularly pertinent in critical sectors such as healthcare, especially during unprecedented events like the COVID-19 pandemic.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"124 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140719980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-07DOI: 10.60087/jaigs.vol03.issue01.p124
Jeff Shuford
The rapid progress in implementing Artificial Intelligence (AI) across various domains such as healthcare decision-making, medical diagnosis, and others has raised significant concerns regarding the fairness and bias embedded within AI systems. This is particularly crucial in sectors like healthcare, employment, criminal justice, credit scoring, and the emerging field of generative AI models (GenAI) producing synthetic media. Such systems can lead to unfair outcomes and perpetuate existing inequalities, including biases ingrained in the synthetic data representation of individuals.This survey paper provides a concise yet comprehensive examination of fairness and bias in AI, encompassing their origins, ramifications, and potential mitigation strategies. We scrutinize sources of bias, including data, algorithmic, and human decision biases, shedding light on the emergent issue of generative AI bias where models may replicate and amplify societal stereotypes. Assessing the societal impact of biased AI systems, we spotlight the perpetuation of inequalities and the reinforcement of harmful stereotypes, especially as generative AI gains traction in shaping public perception through generated content.Various proposed mitigation strategies are explored, with an emphasis on the ethical considerations surrounding their implementation. We stress the necessity of interdisciplinary collaboration to ensure the effectiveness of these strategies. Through a systematic literature review spanning multiple academic disciplines, we define AI bias and its various types, delving into the nuances of generative AI bias. We discuss the adverse effects of AI bias on individuals and society, providing an overview of current approaches to mitigate bias, including data preprocessing, model selection, and post-processing. Unique challenges posed by generative AI models are highlighted, underscoring the importance of tailored strategies to address them effectively.Addressing bias in AI necessitates a holistic approach, involving diverse and representative datasets, enhanced transparency, and accountability in AI systems, and exploration of alternative AI paradigms prioritizing fairness and ethical considerations. This survey contributes to the ongoing discourse on developing fair and unbiased AI systems by outlining the sources, impacts, and mitigation strategies related to AI bias, with a particular focus on the burgeoning field of generative AI.
{"title":"Exploring Ethical Dimensions in AI: Navigating Bias and Fairness in the Field","authors":"Jeff Shuford","doi":"10.60087/jaigs.vol03.issue01.p124","DOIUrl":"https://doi.org/10.60087/jaigs.vol03.issue01.p124","url":null,"abstract":"The rapid progress in implementing Artificial Intelligence (AI) across various domains such as healthcare decision-making, medical diagnosis, and others has raised significant concerns regarding the fairness and bias embedded within AI systems. This is particularly crucial in sectors like healthcare, employment, criminal justice, credit scoring, and the emerging field of generative AI models (GenAI) producing synthetic media. Such systems can lead to unfair outcomes and perpetuate existing inequalities, including biases ingrained in the synthetic data representation of individuals.This survey paper provides a concise yet comprehensive examination of fairness and bias in AI, encompassing their origins, ramifications, and potential mitigation strategies. We scrutinize sources of bias, including data, algorithmic, and human decision biases, shedding light on the emergent issue of generative AI bias where models may replicate and amplify societal stereotypes. Assessing the societal impact of biased AI systems, we spotlight the perpetuation of inequalities and the reinforcement of harmful stereotypes, especially as generative AI gains traction in shaping public perception through generated content.Various proposed mitigation strategies are explored, with an emphasis on the ethical considerations surrounding their implementation. We stress the necessity of interdisciplinary collaboration to ensure the effectiveness of these strategies. Through a systematic literature review spanning multiple academic disciplines, we define AI bias and its various types, delving into the nuances of generative AI bias. We discuss the adverse effects of AI bias on individuals and society, providing an overview of current approaches to mitigate bias, including data preprocessing, model selection, and post-processing. Unique challenges posed by generative AI models are highlighted, underscoring the importance of tailored strategies to address them effectively.Addressing bias in AI necessitates a holistic approach, involving diverse and representative datasets, enhanced transparency, and accountability in AI systems, and exploration of alternative AI paradigms prioritizing fairness and ethical considerations. This survey contributes to the ongoing discourse on developing fair and unbiased AI systems by outlining the sources, impacts, and mitigation strategies related to AI bias, with a particular focus on the burgeoning field of generative AI.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"23 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140732653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-07DOI: 10.60087/jaigs.vol03.issue01.p102
John Deep Smith
This article provides an in-depth exploration of the multifaceted impact of technology on sales performance within B2B companies. It delves into how digital transformation and the integration of advanced technologies such as artificial intelligence, machine learning, and big data analytics have revolutionized traditional sales processes, enhancing efficiency, customer engagement, and ultimately, sales outcomes. The discussion spans several key areas, including the pivotal role of customer relationship management (CRM) systems in improving sales processes, the significance of digital marketing in reaching and engaging with potential customers, and the transformative effects of automation and chatbots in streamlining sales operations and providing superior customer service. The article also touches on the emerging trend of IoT-enabled selling and its potential to offer personalized and proactive sales experiences. Through a series of case studies, the article illustrates successful implementations of technology in B2B sales, showcasing the tangible benefits and improvements in sales performance. However, it also addresses the challenges and barriers to technology adoption, such as resistance to change and integration difficulties, while offering strategies to overcome these obstacles. The future trends section anticipates further advancements in tech-driven sales practices, highlighting the ongoing evolution of the B2B sales landscape driven by technological innovation.
{"title":"The Impact of Technology on Sales Performance in B2B Companies","authors":"John Deep Smith","doi":"10.60087/jaigs.vol03.issue01.p102","DOIUrl":"https://doi.org/10.60087/jaigs.vol03.issue01.p102","url":null,"abstract":"This article provides an in-depth exploration of the multifaceted impact of technology on sales performance within B2B companies. It delves into how digital transformation and the integration of advanced technologies such as artificial intelligence, machine learning, and big data analytics have revolutionized traditional sales processes, enhancing efficiency, customer engagement, and ultimately, sales outcomes. The discussion spans several key areas, including the pivotal role of customer relationship management (CRM) systems in improving sales processes, the significance of digital marketing in reaching and engaging with potential customers, and the transformative effects of automation and chatbots in streamlining sales operations and providing superior customer service. The article also touches on the emerging trend of IoT-enabled selling and its potential to offer personalized and proactive sales experiences. Through a series of case studies, the article illustrates successful implementations of technology in B2B sales, showcasing the tangible benefits and improvements in sales performance. However, it also addresses the challenges and barriers to technology adoption, such as resistance to change and integration difficulties, while offering strategies to overcome these obstacles. The future trends section anticipates further advancements in tech-driven sales practices, highlighting the ongoing evolution of the B2B sales landscape driven by technological innovation.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"58 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140733356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-07DOI: 10.60087/jaigs.vol03.issue01.p142
Md. Rashed Khan
Artificial intelligence (AI) has emerged as a transformative force with profound implications for society, promising significant benefits for individuals with disabilities. While its potential is undeniable, AI also poses inherent risks, including ethical concerns that may exacerbate discrimination against marginalized groups. This paper provides a comprehensive examination of the advantages and drawbacks of AI for people with disabilities, with a particular emphasis on algorithmic biases. These biases, capable of shaping societal structures and influencing decision-making processes, have the potential to perpetuate unfair treatment and discrimination. In light of these challenges, the paper explores potential solutions to address these issues and ensure that AI serves the needs of all individuals, including those with disabilities.
{"title":"Role of AI in Enhancing Accessibility for People with Disabilities","authors":"Md. Rashed Khan","doi":"10.60087/jaigs.vol03.issue01.p142","DOIUrl":"https://doi.org/10.60087/jaigs.vol03.issue01.p142","url":null,"abstract":"Artificial intelligence (AI) has emerged as a transformative force with profound implications for society, promising significant benefits for individuals with disabilities. While its potential is undeniable, AI also poses inherent risks, including ethical concerns that may exacerbate discrimination against marginalized groups. This paper provides a comprehensive examination of the advantages and drawbacks of AI for people with disabilities, with a particular emphasis on algorithmic biases. These biases, capable of shaping societal structures and influencing decision-making processes, have the potential to perpetuate unfair treatment and discrimination. In light of these challenges, the paper explores potential solutions to address these issues and ensure that AI serves the needs of all individuals, including those with disabilities.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"19 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140732678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-04DOI: 10.60087/jaigs.vol03.issue01.p65
Md.mafiqul Islam
This scholarly paper introduces an extensive architectural framework and optimization strategies designed specifically for dynamic resource allocation in edge computing environments, with a focus on AI/ML applications. The rise of edge computing presents a viable solution for managing the computational complexities of AI/ML tasks by utilizing resources in proximity to data sources. Nevertheless, effective resource allocation encounters significant hurdles due to the diverse and ever-changing nature of edge environments. In addressing these challenges, the paper introduces an innovative framework that integrates dynamic resource allocation methodologies with the unique requirements of AI/ML applications. This framework encompasses a range of optimization techniques customized to efficiently distribute resources, taking into account factors such as workload attributes, resource availability, and latency limitations. Through extensive simulations and evaluations, the study showcases the effectiveness of the proposed approach in enhancing resource utilization, reducing latency, and bolstering overall performance for AI/ML workloads within edge computing scenarios.
{"title":"Dynamic Resource Allocation for AI/ML Applications in Edge Computing: Framework Architecture and Optimization Methods","authors":"Md.mafiqul Islam","doi":"10.60087/jaigs.vol03.issue01.p65","DOIUrl":"https://doi.org/10.60087/jaigs.vol03.issue01.p65","url":null,"abstract":"This scholarly paper introduces an extensive architectural framework and optimization strategies designed specifically for dynamic resource allocation in edge computing environments, with a focus on AI/ML applications. The rise of edge computing presents a viable solution for managing the computational complexities of AI/ML tasks by utilizing resources in proximity to data sources. Nevertheless, effective resource allocation encounters significant hurdles due to the diverse and ever-changing nature of edge environments. In addressing these challenges, the paper introduces an innovative framework that integrates dynamic resource allocation methodologies with the unique requirements of AI/ML applications. This framework encompasses a range of optimization techniques customized to efficiently distribute resources, taking into account factors such as workload attributes, resource availability, and latency limitations. Through extensive simulations and evaluations, the study showcases the effectiveness of the proposed approach in enhancing resource utilization, reducing latency, and bolstering overall performance for AI/ML workloads within edge computing scenarios.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140745455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
DevOps is an integration of "Development" (Dev) and "Operations" (Ops) and has emerged as a transformational way of software development, with the objective of enabling rapid and efficient delivery of software. While accepted at larger scales in enterprises, there has been hardly any literature review based on the implementation of DevOps in Small and Medium Enterprises (SMEs) in the United States. This research, therefore, seeks to analyze in detail the challenges, methodologies, and outcomes of implementing DevOps adoption within SMEs in the United States. This research investigates the strategies, benefits, and barriers of SMEs toward the implementation of DevOps using mixed-methods research, including qualitative data from a case study and both interview and quantitative survey data. The results offer a contribution to an understanding of how SMEs adopt DevOps and present valuable knowledge for both practitioners and researchers.
{"title":"Implementing DevOps Adoption within United States SMEs","authors":"Omolola Akinola, Omowunmi Oyerinde, Akintunde Akinola","doi":"10.60087/jaigs.vol03.issue01.p50","DOIUrl":"https://doi.org/10.60087/jaigs.vol03.issue01.p50","url":null,"abstract":"DevOps is an integration of \"Development\" (Dev) and \"Operations\" (Ops) and has emerged as a transformational way of software development, with the objective of enabling rapid and efficient delivery of software. While accepted at larger scales in enterprises, there has been hardly any literature review based on the implementation of DevOps in Small and Medium Enterprises (SMEs) in the United States. This research, therefore, seeks to analyze in detail the challenges, methodologies, and outcomes of implementing DevOps adoption within SMEs in the United States. This research investigates the strategies, benefits, and barriers of SMEs toward the implementation of DevOps using mixed-methods research, including qualitative data from a case study and both interview and quantitative survey data. The results offer a contribution to an understanding of how SMEs adopt DevOps and present valuable knowledge for both practitioners and researchers.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"3 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140746106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-02DOI: 10.60087/jaigs.vol03.issue01.p46
José Gabriel Carrasco Ramírez
Federated learning stands out as a promising approach within the realm of distributed artificial intelligence (AI) systems, facilitating collaborative model training across decentralized devices while safeguarding data privacy. This study presents a thorough investigation into federated learning architecture, covering its foundational design principles, implementation methodologies, and the significant challenges encountered in distributed AI systems. We delve into the fundamental mechanisms underpinning federated learning, elucidating its merits in diverse environments and its prospective applications across various domains. Additionally, we scrutinize the technical complexities associated with deploying federated learning systems, including considerations such as communication efficiency, model aggregation techniques, and security protocols. By amalgamating insights gleaned from recent research endeavors and practical deployments, this study furnishes valuable guidance for both researchers and practitioners aiming to harness federated learning for the development of scalable and privacy-preserving AI solutions.
{"title":"Constructing Executing and Overcoming Challenges in Distributed AI Systems: A Study of Federated Learning Framework","authors":"José Gabriel Carrasco Ramírez","doi":"10.60087/jaigs.vol03.issue01.p46","DOIUrl":"https://doi.org/10.60087/jaigs.vol03.issue01.p46","url":null,"abstract":"Federated learning stands out as a promising approach within the realm of distributed artificial intelligence (AI) systems, facilitating collaborative model training across decentralized devices while safeguarding data privacy. This study presents a thorough investigation into federated learning architecture, covering its foundational design principles, implementation methodologies, and the significant challenges encountered in distributed AI systems. We delve into the fundamental mechanisms underpinning federated learning, elucidating its merits in diverse environments and its prospective applications across various domains. Additionally, we scrutinize the technical complexities associated with deploying federated learning systems, including considerations such as communication efficiency, model aggregation techniques, and security protocols. By amalgamating insights gleaned from recent research endeavors and practical deployments, this study furnishes valuable guidance for both researchers and practitioners aiming to harness federated learning for the development of scalable and privacy-preserving AI solutions.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"20 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140753830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-02DOI: 10.60087/jaigs.vol03.issue01.p85
Harish Padmanaban
With the widespread integration of artificial intelligence (AI) and blockchain technologies, safeguarding privacy has become of paramount importance. These techniques not only ensure the confidentiality of individuals' data but also maintain the integrity and reliability of information. This study offers an introductory overview of AI and blockchain, highlighting their fusion and the subsequent emergence of privacy protection methodologies. It explores various application contexts, such as data encryption, de-identification, multi-tier distributed ledgers, and k-anonymity techniques. Moreover, the paper critically evaluates five essential dimensions of privacy protection systems within AI-blockchain integration: authorization management, access control, data security, network integrity, and scalability. Additionally, it conducts a comprehensive analysis of existing shortcomings, identifying their root causes and suggesting corresponding remedies. The study categorizes and synthesizes privacy protection methodologies based on AI-blockchain application contexts and technical frameworks. In conclusion, it outlines prospective avenues for the evolution of privacy protection technologies resulting from the integration of AI and blockchain, emphasizing the need to enhance efficiency and security for a more comprehensive safeguarding of privacy.
随着人工智能（AI）和区块链技术的广泛融合，保护隐私变得至关重要。这些技术不仅能确保个人数据的保密性，还能维护信息的完整性和可靠性。本研究概述了人工智能和区块链，强调了它们的融合以及随后出现的隐私保护方法。它探讨了各种应用背景，如数据加密、去标识化、多层分布式账本和 k 匿名技术。此外，论文还对人工智能-区块链集成中隐私保护系统的五个基本维度进行了批判性评估：授权管理、访问控制、数据安全、网络完整性和可扩展性。此外，本文还对现有缺陷进行了全面分析，找出了其根本原因，并提出了相应的补救措施。本研究根据人工智能-区块链应用环境和技术框架，对隐私保护方法进行了分类和综合。最后，报告概述了人工智能与区块链融合后隐私保护技术发展的前景，强调需要提高效率和安全性，以更全面地保护隐私。
{"title":"Privacy-Preserving Architectures for AI/ML Applications: Methods, Balances, and Illustrations","authors":"Harish Padmanaban","doi":"10.60087/jaigs.vol03.issue01.p85","DOIUrl":"https://doi.org/10.60087/jaigs.vol03.issue01.p85","url":null,"abstract":"With the widespread integration of artificial intelligence (AI) and blockchain technologies, safeguarding privacy has become of paramount importance. These techniques not only ensure the confidentiality of individuals' data but also maintain the integrity and reliability of information. This study offers an introductory overview of AI and blockchain, highlighting their fusion and the subsequent emergence of privacy protection methodologies. It explores various application contexts, such as data encryption, de-identification, multi-tier distributed ledgers, and k-anonymity techniques. Moreover, the paper critically evaluates five essential dimensions of privacy protection systems within AI-blockchain integration: authorization management, access control, data security, network integrity, and scalability. Additionally, it conducts a comprehensive analysis of existing shortcomings, identifying their root causes and suggesting corresponding remedies. The study categorizes and synthesizes privacy protection methodologies based on AI-blockchain application contexts and technical frameworks. In conclusion, it outlines prospective avenues for the evolution of privacy protection technologies resulting from the integration of AI and blockchain, emphasizing the need to enhance efficiency and security for a more comprehensive safeguarding of privacy.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"137 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140752852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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