arXiv subjects

Supriya Khadka

Publications and source records attributed to Supriya Khadka.

9 recordsLinked to original sources

arXiv2026

Privacy-Preserving Compliance on Public Ledgers via Selective Disclosure Authorization Schemes

Public distributed ledgers enforce integrity through radical transparency, creating tension with data minimization principles required for regulatory compliance. While Zero-Knowledge Proofs (ZKPs) offer a theoretical privacy solution, existing constructions often overlook adversarial constraints in smart contract environments. Specifically, the asynchronous decoupling of off-chain proof generation from on-chain submission introduces front-running and proof-reuse risks in public mempools. In this work, we formalize Selective Disclosure Authorization Schemes (SDAS), a cryptographic primitive for granular and revocable compliance checks on public ledgers without revealing the underlying witness. We define a security model for SDAS, introducing Ledger-Bound Attribute Unlinkability and Context-Aware Sender Binding to capture how valid proofs remain bound to their intended authorization context. To validate sender binding, we present ZK-Compliance, an Ethereum-based instantiation that operationalizes a user-controlled "Grant, Verify, Revoke" lifecycle. We implement the sender-binding component using a 14-constraint Circom circuit that anchors the zero-knowledge proof to the executing on-chain sender address. Our Sepolia evaluation confirms practical viability: browser-based proof generation executes in under 200 ms, and on-chain verification costs 240,512 gas, neutralizing proof reuse by different callers while preserving strict attribute privacy.

cs.CR↗

arXiv2026

Towards a Risk-Cost Model for Financial Adaptive Authentication

Authentication in financial systems remains a uniquely high-stakes security challenge, where even marginal increases in false acceptance can result in catastrophic monetary loss. Existing deployments of adaptive authentication, which combine biometrics, behavioral signals, and contextual risk scoring, remain conceptually fragmented and often prioritize regulatory compliance over explicit economic and adversarial risk modeling. To address this structural imbalance, in this paper we introduce a formal Risk-Cost Model (RCM) for adaptive authentication in financial systems. The RCM provides a principled mathematical foundation that integrates three essential components: (i) cost-sensitive risk functions that explicitly capture fraud loss, opportunity cost, and tail risk through Conditional Value-at-Risk (CVaR); (ii) sequential decision-making mechanisms that adapt to adversarial probing and distributional drift; and (iii) quantifiable privacy and regulatory constraints embedded directly within the optimization objective. By reframing authentication as a constrained dynamic risk-cost optimization problem, the RCM moves beyond static classification and compliance-driven design toward systems that are economically grounded, tail-risk aware, and resilient under adversarial uncertainty.

cs.CR↗

arXiv2026

Designing Augmented Reality for Preschoolers on the Move

Advancements in augmented reality (AR) technologies offer immense potential for mobile experiences. However, most commercial and educational AR systems assume a baseline of predictable user behavior and stationary interaction. Preschoolers and children in early childhood education, specifically ages 3 to 8, are naturally erratic, physically dynamic, and prone to rapid locomotion, making them the ultimate stress test for mobile spatial computing. Through a focused analysis of recent literature on physical activity and spatial learning in AR for preschoolers, this paper identifies points of friction in current mobile deployments. We highlight recurring failures in camera tracking during dynamic movement, physical safety hazards caused by screen-induced distraction, spatial crowding around physical markers, and the privacy risks of continuous environmental surveillance. To address these challenges, we propose AnchorPlay AR, a conceptual prototype for a privacy-preserving, audio-first spatial application. By explicitly separating locomotion from visual tracking, AnchorPlay AR uses audio cues to safely guide movement and reserves visual augmentation for stationary moments, offering a safer framework for preschoolers in constant motion.

cs.HC↗

arXiv2026

Grant, Verify, Revoke: A User-Centric Pattern for Blockchain Compliance

In decentralized web applications, users face an inherent conflict between public verifiability and personal privacy. To participate in regulated on-chain services, users must currently disclose sensitive identity documents to centralized intermediaries, permanently linking real-world identities to public transaction histories. This binary choice between total privacy loss or total exclusion strips users of agency and exposes them to persistent surveillance. In this work, we introduce a Selective Disclosure Framework designed to restore user sovereignty by decoupling eligibility verification from identity revelation. We present ZK-Compliance, a prototype that leverages browser-based zero-knowledge proofs to shift the interaction model, enabling users to prove specific attributes (e.g., "I am over 18") locally without revealing the underlying data. We implement a user-governed Grant, Verify, Revoke lifecycle that transforms the user's mental model of compliance from a permanent data handover into a dynamic, revocable authorization session. Our evaluation shows that client-side proof generation takes under 200ms, enabling a seamless interactive experience on commodity hardware. This work provides early evidence that regulatory compliance need not come at the cost of user privacy or autonomy.

cs.CR↗

arXiv2026

Evaluating Large Language Models' Responses to Sexual and Reproductive Health Queries in Nepali

As Large Language Models (LLMs) become integrated into daily life, they are increasingly used for personal queries, including Sexual and Reproductive Health (SRH), allowing users to chat anonymously without fear of judgment. However, current evaluation methods primarily focus on accuracy, often for objective queries in high-resource languages, and lack criteria to assess usability and safety, especially for low-resource languages and culturally sensitive domains like SRH. This paper introduces LLM Evaluation Framework (LEAF), that conducts assessments across multiple criteria: accuracy, language, usability gaps (including relevance, adequacy, and cultural appropriateness), and safety gaps (safety, sensitivity, and confidentiality). Using the LEAF framework, we assessed 14K SRH queries in Nepali from over 9K users. Responses were manually annotated by SRH experts according to the framework. Results revealed that only 35.1% of the responses were "proper", meaning they were accurate, adequate and had no major usability or safety related gaps. Insights include differences in performance between ChatGPT versions, such as similar accuracy but varying usability and safety aspects. This evaluation highlights significant limitations of current LLMs and underscores the need for improvement. The LEAF Framework is adaptable across domains and languages, particularly where usability and safety are critical, offering a pathway to better address sensitive topics.

cs.CL↗

arXiv2026

Poster: Privacy-Preserving Compliance Checks on Ethereum via Selective Disclosure

Digital identity verification often forces a privacy trade-off, where users must disclose sensitive personal data to prove simple eligibility criteria. As blockchain applications integrate with regulated environments, this over-disclosure creates significant risks of data breaches and surveillance. This work proposes a general Selective Disclosure Framework built on Ethereum, designed to decouple attribute verification from identity revelation. By utilizing client-side zk-SNARKs, the framework enables users to prove specific eligibility predicates without revealing underlying identity documents. We present a case study, ZK-Compliance, which implements a functional Grant, Verify, Revoke lifecycle for age verification. Preliminary results indicate that strict compliance requirements can be satisfied with negligible client-side latency (< 200 ms) while preserving the pseudonymous nature of public blockchains.

cs.CR↗

arXiv2026

Optimizing Nepali PDF Extraction: A Comparative Study of Parser and OCR Technologies

This research compares PDF parsing and Optical Character Recognition (OCR) methods for extracting Nepali content from PDFs. PDF parsing offers fast and accurate extraction but faces challenges with non-Unicode Nepali fonts. OCR, specifically PyTesseract, overcomes these challenges, providing versatility for both digital and scanned PDFs. The study reveals that while PDF parsers are faster, their accuracy fluctuates based on PDF types. In contrast, OCRs, with a focus on PyTesseract, demonstrate consistent accuracy at the expense of slightly longer extraction times. Considering the project's emphasis on Nepali PDFs, PyTesseract emerges as the most suitable library, balancing extraction speed and accuracy.

cs.IR↗

arXiv2026

SoK: Understanding the Pedagogical, Health, Ethical, and Privacy Challenges of Extended Reality in Early Childhood Education

Extended Reality (XR) combines dense sensing, real-time rendering, and close-range interaction, making its use in early childhood education both promising and high risk. To investigate this, we conduct a Systematization of Knowledge (SoK) of 111 peer-reviewed studies with children aged 3-8, quantifying how technical, pedagogical, health, privacy, and equity challenges arise in practice. We found that AR dominates the landscape (73%), focusing primarily on tablets or phones, while VR remains uncommon and typically relies on head mounted displays (HMDs). We integrate these quantitative patterns into a joint risk and attention matrix and an Augmented Human Development (AHD) model that link XR pipeline properties to cognitive load, sensory conflict, and access inequity. Finally, implementing a seven dimension coding scheme on a 0 - 2 scale, we obtain mean scholarly attention scores of 1.56 for pedagogy, 1.04 for privacy (primarily procedural consent), 0.96 for technical reliability, 0.92 for accessibility in low resource contexts, 0.81 for medical and health issues, 0.52 for accessibility for disabilities, and 0.14 for data security practices. This indicates that pedagogy receives the most systematic scrutiny, while data access practices is largely overlooked. We conclude by offering a roadmap for Child-Centered XR that helps HCI researchers and educators move beyond novelty to design systems that are developmentally aligned, secure by default, and accessible to diverse learners.

cs.HC↗

arXiv2026

XR Design Framework for Early Childhood Education

Extended Reality in early childhood education presents high-risk challenges due to children's rapid developmental changes. While augmented and virtual reality offer immersive pedagogical benefits, they often impose excessive cognitive load or sensory conflict. We introduce the Augmented Human Development (AHD) framework to model these interactions through cognitive, sensory, environmental, and developmental parameters. To ground this framework, we conducted a Systematization of Knowledge (SoK) of 111 peer-reviewed studies involving children aged 3 - 8. Our findings, interpreted through the AHD lens, reveal a critical "risk vs. attention gap," where high-impact safety and security risks remain under-researched compared to short-term pedagogical gains.

cs.HC↗