arXiv subjects

Ajay Kumar Shrestha

Publications and source records attributed to Ajay Kumar Shrestha.

At least 19 recordsLinked to original sources

arXiv2026

Negotiating Privacy with Smart Voice Assistants: Risk-Benefit and Control-Acceptance Tensions

Smart Voice assistants (SVAs) are widely adopted by youth, yet privacy decision-making in these environments is often characterized by competing considerations rather than clear-cut preferences. While our prior research has examined privacy risks, benefits, trust, and self-efficacy as distinct predictors of behavior, less attention has been paid to how these factors combine into higher-level tension that shapes privacy outcomes. This study introduces a negotiation-based framework for understanding youth privacy decision-making with SVAs by operationalizing two composite indices: the Risk-Benefit Tension Index (RBTI) and the Control-Acceptance Tension Index (CATI), using survey data from 469 Canadian youth aged 16-24. We examine the distribution of these indices and their relationship with privacy-protective behavior and SVA usage. Results show that both indices are meaningfully associated with protective action. Frequent SVA usage exhibits more benefit-dominant and acceptance-leaning negotiation profiles, suggesting that convenience-driven engagement may come at the expense of perceived control. By reframing privacy decision-making as a process of negotiation rather than inconsistency, this study offers a complementary perspective on the privacy paradox and provides a compact measurement approach for capturing how youth navigate competing privacy pressures in voice-enabled ecosystems.

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arXiv2026

Gender-Based Heterogeneity in Youth Privacy-Protective Behavior for Smart Voice Assistants: Evidence from Multigroup PLS-SEM

This paper investigates how gender shapes privacy decision-making in youth smart voice assistant (SVA) ecosystems. Using survey data from 469 Canadian youths aged 16-24, we apply multigroup Partial Least Squares Structural Equation Modeling to compare males (N=241) and females (N=174) (total N = 415) across five privacy constructs: Perceived Privacy Risks (PPR), Perceived Privacy Benefits (PPBf), Algorithmic Transparency and Trust (ATT), Privacy Self-Efficacy (PSE), and Privacy Protective Behavior (PPB). Results provide exploratory evidence of gender heterogeneity in selected pathways. The direct effect of PPR on PPB is stronger for males (Male: \b{eta} = 0.424; Female: \b{eta} = 0.233; p < 0.1), while the indirect effect of ATT on PPB via PSE is stronger for females (Female: \b{eta} = 0.229; Male: \b{eta} = 0.132; p < 0.1). Descriptive analysis of non-binary (N=15) and prefer-not-to-say participants (N=39) shows lower trust and higher perceived risk than the binary groups, motivating future work with adequately powered gender-diverse samples. Overall, the findings provide exploratory evidence that gender may moderate key privacy pathways, supporting more responsive transparency and control interventions for youth SVA use.

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arXiv2026

From Framework to Practice: Youth Negotiations of Privacy with Smart Voice Assistants Through the PEA-AI Lens

Smart voice assistants (SVAs) have become embedded in the daily lives of youth, introducing complex privacy challenges due to always-on listening, shared device usage, and opaque data practices. This study applies the Privacy-Ethics Alignment in AI (PEA-AI) framework to examine how youth perceive and negotiate privacy within SVAs. Through a survey of 469 Canadian youth (aged 16-24), we measured five privacy constructs: Perceived Privacy Risk (PPR), Perceived Privacy Benefits (PPBf), Algorithmic Transparency and Trust (ATT), Privacy Self-Efficacy (PSE), and Privacy-Protective Behavior (PPB). Results reveal a persistent privacy paradox. While youth express moderate to high privacy concerns (PPR M = 3.61), perceived benefits (PPBf M = 3.00) and protective actions (PPB M = 3.03) remain moderate, with transparency and trust scoring lowest (ATT M = 2.52). Heavy SVA users report higher benefits and lower risk perception than light users. High protective behavior is strongly associated with both high risk perception and high self-efficacy. Qualitative insights from prior focus groups contextualize these patterns, illustrating how youth navigate tensions between convenience, control, and trust. The findings provide actionable design principles for SVA along with implications for multi-stakeholder governance and youth-centered digital literacy.

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arXiv2026

Trust-Based Incentive Mechanisms in Semi-Decentralized Federated Learning Systems

In federated learning (FL), decentralized model training allows multi-ple participants to collaboratively improve a shared machine learning model without exchanging raw data. However, ensuring the integrity and reliability of the system is challenging due to the presence of potentially malicious or faulty nodes that can degrade the model's performance. This paper proposes a novel trust-based incentive mechanism designed to evaluate and reward the quality of contributions in FL systems. By dynamically assessing trust scores based on fac-tors such as data quality, model accuracy, consistency, and contribution fre-quency, the system encourages honest participation and penalizes unreliable or malicious behavior. These trust scores form the basis of an incentive mechanism that rewards high-trust nodes with greater participation opportunities and penal-ties for low-trust participants. We further explore the integration of blockchain technology and smart contracts to automate the trust evaluation and incentive distribution processes, ensuring transparency and decentralization. Our proposed theoretical framework aims to create a more robust, fair, and transparent FL eco-system, reducing the risks posed by untrustworthy participants.

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arXiv2026

Privacy by Voice: Modeling Youth Privacy-Protective Behavior in Smart Voice Assistants

Smart Voice Assistants (SVAs) are deeply embedded in the lives of youth, yet the mechanisms driving the privacy-protective behaviors among young users remain poorly understood. This study investigates how Canadian youth (aged 16-24) negotiate privacy with SVAs by developing and testing a structural model grounded in five key constructs: perceived privacy risks (PPR), perceived benefits (PPBf), algorithmic transparency and trust (ATT), privacy self-efficacy (PSE), and privacy-protective behaviors (PPB). A cross-sectional survey of N=469 youth was analyzed using partial least squares structural equation modeling. Results reveal that PSE is the strongest predictor of PPB, while the effect of ATT on PPB is fully mediated by PSE. This identifies a critical efficacy gap, where youth's confidence must first be built up for them to act. The model confirms that PPBf directly discourages protective action, yet also indirectly fosters it by slightly boosting self-efficacy. These findings empirically validate and extend earlier qualitative work, quantifying how policy overload and hidden controls erode the self-efficacy necessary for protective action. This study contributes an evidence-based pathway from perception to action and translates it into design imperatives that empower young digital citizens without sacrificing the utility of SVAs.

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arXiv2026

Balancing Usability and Compliance in AI Smart Devices: A Privacy-by-Design Audit of Google Home, Alexa, and Siri

This paper investigates the privacy and usability of AI-enabled smart devices commonly used by youth, focusing on Google Home Mini, Amazon Alexa, and Apple Siri. While these devices provide convenience and efficiency, they also raise privacy and transparency concerns due to their always-listening design and complex data management processes. The study proposes and applies a combined framework of Heuristic Evaluation, Personal Information Protection and Electronic Documents Act (PIPEDA) Compliance Assessment, and Youth-Centered Usability Testing to assess whether these devices align with Privacy-by-Design principles and support meaningful user control. Results show that Google Home achieved the highest usability score, while Siri scored highest in regulatory compliance, indicating a trade-off between user convenience and privacy protection. Alexa demonstrated clearer task navigation but weaker transparency in data retention. Findings suggest that although youth may feel capable of managing their data, their privacy self-efficacy remains limited by technical design, complex settings, and unclear data policies. The paper concludes that enhancing transparency, embedding privacy guidance during onboarding, and improving policy alignment are critical steps toward ensuring that smart devices are both usable and compliant with privacy standards that protect young users.

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arXiv2026

Convenience vs. Control: A Qualitative Study of Youth Privacy with Smart Voice Assistants

Smart voice assistants (SVAs) are embedded in the daily lives of youth, yet their privacy controls often remain opaque and difficult to manage. Through five semi-structured focus groups (N=26) with young Canadians (ages 16-24), we investigate how perceived privacy risks (PPR) and benefits (PPBf) intersect with algorithmic transparency and trust (ATT) and privacy self-efficacy (PSE) to shape privacy-protective behaviors (PPB). Our analysis reveals that policy overload, fragmented settings, and unclear data retention undermine self-efficacy and discourage protective actions. Conversely, simple transparency cues were associated with greater confidence without diminishing the utility of hands-free tasks and entertainment. We synthesize these findings into a qualitative model in which transparency friction erodes PSE, which in turn weakens PPB. From this model, we derive actionable design guidance for SVAs, including a unified privacy hub, plain-language "data nutrition" labels, clear retention defaults, and device-conditional micro-tutorials. This work foregrounds youth perspectives and offers a path for SVA governance and design that empowers young digital citizens while preserving convenience.

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arXiv2026

Toward Youth-Centered Privacy-by-Design in Smart Devices: A Systematic Review

This literature review evaluates privacy-by-design frameworks, tools, and policies intended to protect youth in AI-enabled smart devices using a PRISMA-guided workflow. Sources from major academic and grey-literature repositories from the past decade were screened. The search identified 2,216 records; after deduplication and screening, 645 articles underwent eligibility assessment, and 122 were included for analysis. The corpus was organized along three thematic categories: technical solutions, policy/regulatory measures, and education/awareness strategies. Findings reveal that while technical interventions such as on-device processing, federated learning, and lightweight encryption significantly reduce data exposure, their adoption remains limited. Policy frameworks, including the EU's GDPR, the UK Age-Appropriate Design Code, and Canada's PIPEDA, provide important baselines but are hindered by gaps in enforcement and age-appropriate design obligations, while educational initiatives are rarely integrated systematically into curricula. Overall, the corpus skews toward technical solutions (67%) relative to policy (21%) and education (12%), indicating an implementation gap outside the technical domain. To address these challenges, we recommend a multi-stakeholder model in which policymakers, manufacturers, and educators co-develop inclusive, transparent, and context-sensitive privacy ecosystems. This work advances discourse on youth data protection by offering empirically grounded insights and actionable recommendations for the design of ethical, privacy-preserving AI systems tailored to young users.

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arXiv2025

Covert Surveillance in Smart Devices: A SCOUR Framework Analysis of Youth Privacy Implications

This paper investigates how smart devices covertly capture private conversations and discusses in more in-depth the implications of this for youth privacy. Using a structured review guided by the PRISMA methodology, the analysis focuses on privacy concerns, data capture methods, data storage and sharing practices, and proposed technical mitigations. To structure and synthesize findings, we introduce the SCOUR framework, encompassing Surveillance mechanisms, Consent and awareness, Operational data flow, Usage and exploitation, and Regulatory and technical safeguards. Findings reveal that smart devices have been covertly capturing personal data, especially with smart toys and voice-activated smart gadgets built for youth. These issues are worsened by unclear data collection practices and insufficient transparency in smart device applications. Balancing privacy and utility in smart devices is crucial, as youth are becoming more aware of privacy breaches and value their personal data more. Strategies to improve regulatory and technical safeguards are also provided. The review identifies research gaps and suggests future directions. The limitations of this literature review are also explained. The findings have significant implications for policy development and the transparency of data collection for smart devices.

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arXiv2025

Ethical AI for Young Digital Citizens: A Call to Action on Privacy Governance

The rapid expansion of Artificial Intelligence (AI) in digital platforms used by youth has created significant challenges related to privacy, autonomy, and data protection. While AI-driven personalization offers enhanced user experiences, it often operates without clear ethical boundaries, leaving young users vulnerable to data exploitation and algorithmic biases. This paper presents a call to action for ethical AI governance, advocating for a structured framework that ensures youth-centred privacy protections, transparent data practices, and regulatory oversight. We outline key areas requiring urgent intervention, including algorithmic transparency, privacy education, parental data-sharing ethics, and accountability measures. Through this approach, we seek to empower youth with greater control over their digital identities and propose actionable strategies for policymakers, AI developers, and educators to build a fairer and more accountable AI ecosystem.

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arXiv2025

Privacy Ethics Alignment in AI: A Stakeholder-Centric Framework for Ethical AI

The increasing integration of artificial intelligence (AI) in digital ecosystems has reshaped privacy dynamics, particularly for young digital citizens navigating data-driven environments. This study explores evolving privacy concerns across three key stakeholder groups-young digital citizens, parents/educators, and AI professionals-and assesses differences in data ownership, trust, transparency, parental mediation, education, and risk-benefit perceptions. Employing a grounded theory methodology, this research synthesizes insights from key participants through structured surveys, qualitative interviews, and focus groups to identify distinct privacy expectations. Young digital citizens emphasized autonomy and digital agency, while parents and educators prioritized oversight and AI literacy. AI professionals focused on balancing ethical design with system performance. The analysis revealed significant gaps in transparency and digital literacy, underscoring the need for inclusive, stakeholder-driven privacy frameworks. Drawing on comparative thematic analysis, this study introduces the Privacy-Ethics Alignment in AI (PEA-AI) model, which conceptualizes privacy decision-making as a dynamic negotiation among stakeholders. By aligning empirical findings with governance implications, this research provides a scalable foundation for adaptive, youth-centered AI privacy governance.

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arXiv2025

Toward Ethical AI: A Qualitative Analysis of Stakeholder Perspectives

As Artificial Intelligence (AI) systems become increasingly integrated into various aspects of daily life, concerns about privacy and ethical accountability are gaining prominence. This study explores stakeholder perspectives on privacy in AI systems, focusing on educators, parents, and AI professionals. Using qualitative analysis of survey responses from 227 participants, the research identifies key privacy risks, including data breaches, ethical misuse, and excessive data collection, alongside perceived benefits such as personalized services, enhanced efficiency, and educational advancements. Stakeholders emphasized the need for transparency, privacy-by-design, user empowerment, and ethical oversight to address privacy concerns effectively. The findings provide actionable insights into balancing the benefits of AI with robust privacy protections, catering to the diverse needs of stakeholders. Recommendations include implementing selective data use, fostering transparency, promoting user autonomy, and integrating ethical principles into AI development. This study contributes to the ongoing discourse on ethical AI, offering guidance for designing privacy-centric systems that align with societal values and build trust among users. By addressing privacy challenges, this research underscores the importance of developing AI technologies that are not only innovative but also ethically sound and responsive to the concerns of all stakeholders.

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arXiv2025

Investigation of the Privacy Concerns in AI Systems for Young Digital Citizens: A Comparative Stakeholder Analysis

The integration of Artificial Intelligence (AI) systems into technologies used by young digital citizens raises significant privacy concerns. This study investigates these concerns through a comparative analysis of stakeholder perspectives. A total of 252 participants were surveyed, with the analysis focusing on 110 valid responses from parents/educators and 100 from AI professionals after data cleaning. Quantitative methods, including descriptive statistics and Partial Least Squares Structural Equation Modeling, examined five validated constructs: Data Ownership and Control, Parental Data Sharing, Perceived Risks and Benefits, Transparency and Trust, and Education and Awareness. Results showed Education and Awareness significantly influenced data ownership and risk assessment, while Data Ownership and Control strongly impacted Transparency and Trust. Transparency and Trust, along with Perceived Risks and Benefits, showed minimal influence on Parental Data Sharing, suggesting other factors may play a larger role. The study underscores the need for user-centric privacy controls, tailored transparency strategies, and targeted educational initiatives. Incorporating diverse stakeholder perspectives offers actionable insights into ethical AI design and governance, balancing innovation with robust privacy protections to foster trust in a digital age.

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arXiv2024

Navigating AI to Unpack Youth Privacy Concerns: An In-Depth Exploration and Systematic Review

This systematic literature review investigates perceptions, concerns, and expectations of young digital citizens regarding privacy in artificial intelligence (AI) systems, focusing on social media platforms, educational technology, gaming systems, and recommendation algorithms. Using a rigorous methodology, the review started with 2,000 papers, narrowed down to 552 after initial screening, and finally refined to 108 for detailed analysis. Data extraction focused on privacy concerns, data-sharing practices, the balance between privacy and utility, trust factors in AI, transparency expectations, and strategies to enhance user control over personal data. Findings reveal significant privacy concerns among young users, including a perceived lack of control over personal information, potential misuse of data by AI, and fears of data breaches and unauthorized access. These issues are worsened by unclear data collection practices and insufficient transparency in AI applications. The intention to share data is closely associated with perceived benefits and data protection assurances. The study also highlights the role of parental mediation and the need for comprehensive education on data privacy. Balancing privacy and utility in AI applications is crucial, as young digital citizens value personalized services but remain wary of privacy risks. Trust in AI is significantly influenced by transparency, reliability, predictable behavior, and clear communication about data usage. Strategies to improve user control over personal data include access to and correction of data, clear consent mechanisms, and robust data protection assurances. The review identifies research gaps and suggests future directions, such as longitudinal studies, multicultural comparisons, and the development of ethical AI frameworks. The findings have significant implications for policy development and educational initiatives

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arXiv2024

Exploring User Acceptance of Blockchain-Based Student Certificate Sharing System: A Study on Non Fungible Token (NFT) Utilization

Blockchain technology has emerged as a transformative tool for data management in a variety of industries, including fintech, research and healthcare. We have developed a workable blockchain based system that utilizes non fungible tokens NFTs to tokenize and prove ownership of the academic institutions credentials. This makes it easier to create provenance and ownership documentation for academic data and meta credentials. This system enables the secure sharing of academic information while maintaining control, offering incentives for collaboration, and granting users full transparency and control over data access. While the initial adoption of these systems is crucial for ongoing service usage, the exploration of the user acceptance behavioural model remains limited in the existing literature. In this paper, we build upon the Technology Acceptance Model TAM, incorporating additional elements to scrutinize the impact of perceived ease of use, perceived usability, and attitude towards the system on the intention to use a blockchain based academic data and meta credentials sharing system. The research, grounded in user evaluations of a prototype, employs a TAM validated questionnaire. Results indicate that individual constructs notably affect the intention to use the system, and their collective impact is statistically significant. Specifically, perceived ease of use is the sole factor with an insignificant influence on the intention to use. The paper underscores the dominant influence of attitude towards the system on perceived usefulness. It concludes with a discussion on the implications of these findings within the context of blockchain based academic data and meta credentials sharing, incorporating NFTs for ownership definition.

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arXiv2023

Student Certificate Sharing System Using Blockchain and NFTs

In this paper, we propose a certificate sharing system based on blockchain that gives students authority and control over their academic certificates. Our strategy involves developing blockchain-based NFT certifications that can be shared with institutions or employers using blockchain addresses. Students may access the data created by each individual institute in a single platform, filter the view of the relevant courses according to their requirements, and mint their certificate metadata as NFTs. This method provides accountability of access, comprehensive records that are permanently maintained in IPFS, and verifiable provenance for creating, distributing, and accessing certificates. It also makes it possible to share certificates more safely and efficiently. By incorporating trust factors through data provenance, our system provides a countermeasure against issues such as fake and duplicate certificates. It addresses the challenge of the traditional certificate verification processes, which are lengthy manual process. With this system, students can manage and validate their academic credentials from multiple institutions in one location while ensuring authenticity and confidentiality using digital signatures and hashing for data protection against unauthorized access. Overall, our suggested system ensures data safety, accountability, and confidentiality while offering a novel approach to certificate distribution.

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arXiv2023

Enhancing Scalability and Reliability in Semi-Decentralized Federated Learning With Blockchain: Trust Penalization and Asynchronous Functionality

The paper presents an innovative approach to address the challenges of scalability and reliability in Distributed Federated Learning by leveraging the integration of blockchain technology. The paper focuses on enhancing the trustworthiness of participating nodes through a trust penalization mechanism while also enabling asynchronous functionality for efficient and robust model updates. By combining Semi-Decentralized Federated Learning with Blockchain (SDFL-B), the proposed system aims to create a fair, secure and transparent environment for collaborative machine learning without compromising data privacy. The research presents a comprehensive system architecture, methodologies, experimental results, and discussions that demonstrate the advantages of this novel approach in fostering scalable and reliable SDFL-B systems.

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arXiv2023

Blockchain-Based Federated Learning: Incentivizing Data Sharing and Penalizing Dishonest Behavior

With the increasing importance of data sharing for collaboration and innovation, it is becoming more important to ensure that data is managed and shared in a secure and trustworthy manner. Data governance is a common approach to managing data, but it faces many challenges such as data silos, data consistency, privacy, security, and access control. To address these challenges, this paper proposes a comprehensive framework that integrates data trust in federated learning with InterPlanetary File System, blockchain, and smart contracts to facilitate secure and mutually beneficial data sharing while providing incentives, access control mechanisms, and penalizing any dishonest behavior. The experimental results demonstrate that the proposed model is effective in improving the accuracy of federated learning models while ensuring the security and fairness of the data-sharing process. The research paper also presents a decentralized federated learning platform that successfully trained a CNN model on the MNIST dataset using blockchain technology. The platform enables multiple workers to train the model simultaneously while maintaining data privacy and security. The decentralized architecture and use of blockchain technology allow for efficient communication and coordination between workers. This platform has the potential to facilitate decentralized machine learning and support privacy-preserving collaboration in various domains.

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