arXiv subjects

Sanchari Das

Publications and source records attributed to Sanchari Das.

At least 37 records · Page 2Linked to original sources

arXiv2025

SoK: A Systematic Review of Context- and Behavior-Aware Adaptive Authentication in Mobile Environments

As mobile computing becomes central to digital interaction, researchers have turned their attention to adaptive authentication for its real-time, context- and behavior-aware verification capabilities. However, many implementations remain fragmented, inconsistently apply intelligent techniques, and fall short of user expectations. In this Systematization of Knowledge (SoK), we analyze 41 peer-reviewed studies since 2011 that focus on adaptive authentication in mobile environments. Our analysis spans seven dimensions: privacy and security models, interaction modalities, user behavior, risk perception, implementation challenges, usability needs, and machine learning frameworks. Our findings reveal a strong reliance on machine learning (64.3%), especially for continuous authentication (61.9%) and unauthorized access prevention (54.8%). AI-driven approaches such as anomaly detection (57.1%) and spatio-temporal analysis (52.4%) increasingly shape the interaction landscape, alongside growing use of sensor-based and location-aware models.

cs.CR↗

arXiv2025

TRIDENT -- A Three-Tier Privacy-Preserving Propaganda Detection Model in Mobile Networks using Transformers, Adversarial Learning, and Differential Privacy

The proliferation of propaganda on mobile platforms raises critical concerns around detection accuracy and user privacy. To address this, we propose TRIDENT - a three-tier propaganda detection model implementing transformers, adversarial learning, and differential privacy which integrates syntactic obfuscation and label perturbation to mitigate privacy leakage while maintaining propaganda detection accuracy. TRIDENT leverages multilingual back-translation to introduce semantic variance, character-level noise, and entity obfuscation for differential privacy enforcement, and combines these techniques into a unified defense mechanism. Using a binary propaganda classification dataset, baseline transformer models (BERT, GPT-2) we achieved F1 scores of 0.89 and 0.90. Applying TRIDENT's third-tier defense yields a reduced but effective cumulative F1 of 0.83, demonstrating strong privacy protection across mobile ML deployments with minimal degradation.

cs.CR↗

arXiv2025

Multi-Tool Analysis of User Interface & Accessibility in Deployed Web-Based Chatbots

In this work, we present a multi-tool evaluation of 106 deployed web-based chatbots, across domains like healthcare, education and customer service, comprising both standalone applications and embedded widgets using automated tools (Google Lighthouse, PageSpeed Insights, SiteImprove Accessibility Checker) and manual audits (Microsoft Accessibility Insights). Our analysis reveals that over 80% of chatbots exhibit at least one critical accessibility issue, and 45% suffer from missing semantic structures or ARIA role misuse. Furthermore, we found that accessibility scores correlate strongly across tools (e.g., Lighthouse vs PageSpeed Insights, r = 0.861), but performance scores do not (r = 0.436), underscoring the value of a multi-tool approach. We offer a replicable evaluation insights and actionable recommendations to support the development of user-friendly conversational interfaces.

cs.HC↗

arXiv2025

POSTER: A Multi-Signal Model for Detecting Evasive Smishing

Smishing, or SMS-based phishing, poses an increasing threat to mobile users by mimicking legitimate communications through culturally adapted, concise, and deceptive messages, which can result in the loss of sensitive data or financial resources. In such, we present a multi-channel smishing detection model that combines country-specific semantic tagging, structural pattern tagging, character-level stylistic cues, and contextual phrase embeddings. We curated and relabeled over 84,000 messages across five datasets, including 24,086 smishing samples. Our unified architecture achieves 97.89% accuracy, an F1 score of 0.963, and an AUC of 99.73%, outperforming single-stream models by capturing diverse linguistic and structural cues. This work demonstrates the effectiveness of multi-signal learning in robust and region-aware phishing.

cs.LG↗

arXiv2025

"Watch My Health, Not My Data": Understanding Perceptions, Barriers, Emotional Impact, & Coping Strategies Pertaining to IoT Privacy and Security in Health Monitoring for Older Adults

The proliferation of "Internet of Things (IoT)" provides older adults with critical support for "health monitoring" and independent living, yet significant concerns about security and privacy persist. In this paper, we report on these issues through a two-phase user study, including a survey (N = 22) and semi-structured interviews (n = 9) with adults aged 65+. We found that while 81.82% of our participants are aware of security features like "two-factor authentication (2FA)" and encryption, 63.64% express serious concerns about unauthorized access to sensitive health data. Only 13.64% feel confident in existing protections, citing confusion over "data sharing policies" and frustration with "complex security settings" which lead to distrust and anxiety. To cope, our participants adopt various strategies, such as relying on family or professional support and limiting feature usage leading to disengagement. Thus, we recommend "adaptive security mechanisms," simplified interfaces, and real-time transparency notifications to foster trust and ensure "privacy and security by design" in IoT health systems for older adults.

cs.HC↗

arXiv2025

"It's Like Not Being Able to Read and Write": Narrowing the Digital Divide for Older Adults and Leveraging the Role of Digital Educators in Ireland

As digital services increasingly replace traditional analogue systems, ensuring that older adults are not left behind is critical to fostering inclusive access. This study explores how digital educators support older adults in developing essential digital skills, drawing insights from interviews with $34$ educators in Ireland. These educators, both professional and volunteer, offer instruction through a range of formats, including workshops, remote calls, and in-person sessions. Our findings highlight the importance of personalized, step-by-step guidance tailored to older adults' learning needs, as well as fostering confidence through hands-on engagement with technology. Key challenges identified include limited transportation options, poor internet connectivity, outdated devices, and a lack of familial support for learning. To address these barriers, we propose enhanced public funding, expanded access to resources, and sustainable strategies such as providing relevant and practical course materials. Additionally, innovative tools like simulated online platforms for practicing digital transactions can help reduce anxiety and enhance digital literacy among older adults. This study underscores the vital role that digital educators play in bridging the digital divide, creating a more inclusive, human-centered approach to digital learning for older adults.

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arXiv2025

Natural Language Processing of Privacy Policies: A Survey

Natural Language Processing (NLP) is an essential subset of artificial intelligence. It has become effective in several domains, such as healthcare, finance, and media, to identify perceptions, opinions, and misuse, among others. Privacy is no exception, and initiatives have been taken to address the challenges of usable privacy notifications to users with the help of NLP. To this aid, we conduct a literature review by analyzing 109 papers at the intersection of NLP and privacy policies. First, we provide a brief introduction to privacy policies and discuss various facets of associated problems, which necessitate the application of NLP to elevate the current state of privacy notices and disclosures to users. Subsequently, we a) provide an overview of the implementation and effectiveness of NLP approaches for better privacy policy communication; b) identify the methodologies that can be further enhanced to provide robust privacy policies; and c) identify the gaps in the current state-of-the-art research. Our systematic analysis reveals that several research papers focus on annotating and classifying privacy texts for analysis but need to adequately dwell on other aspects of NLP applications, such as summarization. More specifically, ample research opportunities exist in this domain, covering aspects such as corpus generation, summarization vectors, contextualized word embedding, identification of privacy-relevant statement categories, fine-grained classification, and domain-specific model tuning.

cs.CL↗

arXiv2024

Design of Secure, Privacy-focused, and Accessible E-Payment Applications for Older Adults

E-payments are essential for transactional convenience in today's digital economy and are becoming increasingly important for older adults, emphasizing the need for enhanced security, privacy, and usability. To address this, we conducted a survey-based study with 400 older adults aged 60 and above to evaluate a high-fidelity prototype of an e-payment mobile application, which included features such as multi-factor authentication (MFA) and QR code-based recipient addition. Based on our findings, we developed a tailored \b{eta} version of the application to meet the specific needs of this demographic. Notably, approximately 91% of participants preferred traditional knowledge-based and single-mode authentication compared to expert-recommended MFA. We concluded by providing recommendations aimed at developing inclusive e-payment solutions that address the security, privacy, and usability requirements of older adults.

cs.CY↗

arXiv2024

'Debunk-It-Yourself': Health Professionals' Strategies for Responding to Misinformation on TikTok

Misinformation is "sticky" in nature, requiring a considerable effort to undo its influence. One such effort is debunking or exposing the falsity of information. As an abundance of misinformation is on social media, platforms do bear some debunking responsibility in order to preserve their trustworthiness as information providers. A subject of interpretation, platforms poorly meet this responsibility and allow dangerous health misinformation to influence many of their users. This open route to harm did not sit well with health professional users, who recently decided to take the debunking into their own hands. To study this individual debunking effort - which we call 'Debunk-It-Yourself (DIY)' - we conducted an exploratory survey n=14 health professionals who wage a misinformation counter-influence campaign through videos on TikTok. We focused on two topics, nutrition and mental health, which are the ones most often subjected to misinformation on the platform. Our thematic analysis reveals that the counterinfluence follows a common process of initiation, selection, creation, and "stitching" or duetting a debunking video with a misinformation video. The 'Debunk-It-Yourself' effort was underpinned by three unique aspects: (i) it targets trending misinformation claims perceived to be of direct harm to people's health; (ii) it offers a symmetric response to the misinformation; and (iii) it is strictly based on scientific evidence and claimed clinical experience. Contrasting the 'Debunk-It-Yourself' effort with the one TikTok and other platforms (reluctantly) put in moderation, we offer recommendations for a structured response against the misinformation's influence by the users themselves.

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arXiv2024

Enhancing Personalised Cybersecurity Guidance for Older Adults in Ireland

The term `Digital Divide' emerged in the mid-1990s, highlighting the gap between those with access to emerging information technologies and those without. This gap persists for older adults even in the 21st century. To address this, our study focused on how older adults in Ireland can feel safer online. We conducted a two-phase study. In Phase I, 58 participants used Dot Voting to identify top cyber-security priorities, including password management, privacy, and avoiding scams. This informed Phase II, where we held focus groups with 31 participants from rural and urban communities in Ireland. Researchers provided tailored advice through presentations and leaflets, followed by open discussions. Our findings show that, despite being highly aware of cyber-scams, older adults remain very concerned about them. Participants expressed hesitation about using online password managers and two-factor authentication but valued advice on privacy and tools that can help them feel more in control online.

cs.HC↗

arXiv2024

Securing the Web: Analysis of HTTP Security Headers in Popular Global Websites

The surge in website attacks, including Denial of Service (DoS), Cross-Site Scripting (XSS), and Clickjacking, underscores the critical need for robust HTTPS implementation-a practice that, alarmingly, remains inadequately adopted. Regarding this, we analyzed HTTP security headers across N=3,195 globally popular websites. Initially, we employed automated categorization using Google NLP to organize these websites into functional categories and validated this categorization through manual verification using Symantec Sitereview. Subsequently, we assessed HTTPS implementation across these websites by analyzing security factors, including compliance with HTTP Strict Transport Security (HSTS) policies, Certificate Pinning practices, and other security postures using the Mozilla Observatory. Our analysis revealed over half of the websites examined (55.66%) received a dismal security grade of 'F' and most websites scored low for various metrics, which is indicative of weak HTTP header implementation. These low scores expose multiple issues such as weak implementation of Content Security Policies (CSP), neglect of HSTS guidelines, and insufficient application of Subresource Integrity (SRI). Alarmingly, healthcare websites (n=59) are particularly concerning; despite being entrusted with sensitive patient data and obligations to comply with data regulations, these sites recorded the lowest average score (18.14). We conclude by recommending that developers should prioritize secure redirection strategies and use implementation ease as a guide when deciding where to focus their development efforts.

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arXiv2024

Dual-Technique Privacy & Security Analysis for E-Commerce Websites Through Automated and Manual Implementation

As e-commerce continues to expand, the urgency for stronger privacy and security measures becomes increasingly critical, particularly on platforms frequented by younger users who are often less aware of potential risks. In our analysis of 90 US-based e-commerce websites, we employed a dual-technique approach, combining automated tools with manual evaluations. Tools like CookieServe and PrivacyCheck revealed that 38.5% of the websites deployed over 50 cookies per session, many of which were categorized as unnecessary or unclear in function, posing significant risks to users' Personally Identifiable Information (PII). Our manual assessment further uncovered critical gaps in standard security practices, including the absence of mandatory multi-factor authentication (MFA) and breach notification protocols. Additionally, we observed inadequate input validation, which compromises the integrity of user data and transactions. Based on these findings, we recommend targeted improvements to privacy policies, enhanced transparency in cookie usage, and the implementation of stronger authentication protocols. These measures are essential for ensuring compliance with CCPA and COPPA, thereby fostering more secure online environments, particularly for younger users.

cs.CR↗

arXiv2024

Evaluating Privacy Measures in Healthcare Apps Predominantly Used by Older Adults

The widespread adoption of telehealth systems has led to a significant increase in the use of healthcare apps among older adults, but this rapid growth has also heightened concerns about the privacy of their health information. While HIPAA in the US and GDPR in the EU establish essential privacy protections for health information, limited research exists on the effectiveness of healthcare app privacy policies, particularly those used predominantly by older adults. To address this, we evaluated 28 healthcare apps across multiple dimensions, including regulatory compliance, data handling practices, and privacy-focused usability. To do this, we created a Privacy Risk Assessment Framework (PRAF) and used it to evaluate the privacy risks associated with these healthcare apps designed for older adults. Our analysis revealed significant gaps in compliance with privacy standards to such, only 25% of apps explicitly state compliance with HIPAA, and only 18% mention GDPR. Surprisingly, 79% of these applications lack breach protocols, putting older adults at risk in the event of a data breach.

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arXiv2024

Exploring Older Adults' Perceptions and Experiences with Online Dating

The rise of online dating apps has transformed how individuals connect and seek companionship, with an increase in usage among older adults. While these platforms offer opportunities for emotional support and social connection, they also present significant challenges, including a concerning trend of online dating scams targeting this demographic. To address these issues, we conducted a semi-structured interview focused on the online dating experiences of older adults (65+). Initially, we conducted a pre-screening survey, followed by focused semi-structured interviews with 11 of the selected older adults. Through this study, we investigate older adults' security and privacy concerns, the significance of design elements and accessibility, and identify areas needing improvement. Our findings reveal challenges such as deceptive practices, including catfishing and fraud, concerns over disclosing sensitive information, non-inclusive app design features, and the need for more informative visualization of match requests. We offer recommendations for enhanced identity verification, inclusive privacy controls by app developers, and increased digital literacy efforts to enable older adults to navigate these platforms safely and confidently.

cs.CY↗

arXiv2024

Towards Perceived Security, Perceived Privacy, and the Universal Design of E-Payment Applications

With the growth of digital monetary transactions and cashless payments, encouraged by the COVID-19 pandemic, use of e-payment applications is on the rise. It is thus imperative to understand and evaluate the current posture of e-payment applications from three major user-facing angles: security, privacy, and usability. To this, we created a high-fidelity prototype of an e-payment application that encompassed features that we wanted to test with users. We then conducted a pilot study where we recruited 12 participants who tested our prototype. We find that both security and privacy are important for users of e-payment applications. Additionally, some participants perceive the strength of security and privacy based on the usability of the application. We provide recommendations such as universal design of e-payment applications.

cs.HC↗

arXiv2024

Understanding Professional Needs to Create Privacy-Preserving and Secure Emergent Digital Artworks

In recent years, immersive art installations featuring interactive artworks have been on the rise. These installations are an integral part of museums and art centers like selfie museums, teamLab Borderless, ARTECHOUSE, and Meow Wolf. Moreover, immersive art have also been increasingly incorporated into traditional museums as well. However, immersive art requires active user participation and often captures information from viewers and participants through cameras, sensors, microphones, embodied interaction devices, surveillance, and kinetic mirrors. Therefore, we propose a new line of research to examine the security and privacy postures of immersive artworks. In our pilot study, we conducted a semi-structured interview with five experienced practitioners from either the art (2) or cybersecurity (3) fields. Our aim was to understand their current security and privacy practices, along with their needs when it comes to immersive art. From their responses, we created a list of security and privacy parameters, such as, providing opt-in mechanics for data collection, knowledge of data collection tools such as proximity sensors, and creating security awareness amongst participants by communicating security protocols and threat models. These parameters allow us to build privacy-preserving, secure, and accessible software for individuals working in media arts, who often have no background on security and privacy. In the future, we plan to utilize these parameters to develop software in response to those needs and then host an art exhibition of immersive artworks utilizing the platform.

cs.HC↗

arXiv2024

The State of Lithium-Ion Battery Health Prognostics in the CPS Era

Lithium-ion batteries (Li-ion) have revolutionized energy storage technology, becoming integral to our daily lives by powering a diverse range of devices and applications. Their high energy density, fast power response, recyclability, and mobility advantages have made them the preferred choice for numerous sectors. This paper explores the seamless integration of Prognostics and Health Management within batteries, presenting a multidisciplinary approach that enhances the reliability, safety, and performance of these powerhouses. Remaining useful life (RUL), a critical concept in prognostics, is examined in depth, emphasizing its role in predicting component failure before it occurs. The paper reviews various RUL prediction methods, from traditional models to cutting-edge data-driven techniques. Furthermore, it highlights the paradigm shift toward deep learning architectures within the field of Li-ion battery health prognostics, elucidating the pivotal role of deep learning in addressing battery system complexities. Practical applications of PHM across industries are also explored, offering readers insights into real-world implementations.This paper serves as a comprehensive guide, catering to both researchers and practitioners in the field of Li-ion battery PHM.

cs.LG↗

arXiv2023

Evaluating the Security and Privacy Risk Postures of Virtual Assistants

Virtual assistants (VAs) have seen increased use in recent years due to their ease of use for daily tasks. Despite their growing prevalence, their security and privacy implications are still not well understood. To address this gap, we conducted a study to evaluate the security and privacy postures of eight widely used voice assistants: Alexa, Braina, Cortana, Google Assistant, Kalliope, Mycroft, Hound, and Extreme. We used three vulnerability testing tools, AndroBugs, RiskInDroid, and MobSF, to assess the security and privacy of these VAs. Our analysis focused on five areas: code, access control, tracking, binary analysis, and sensitive data confidentiality. The results revealed that these VAs are vulnerable to a range of security threats, including not validating SSL certificates, executing raw SQL queries, and using a weak mode of the AES algorithm. These vulnerabilities could allow malicious actors to gain unauthorized access to users' personal information. This study is a first step toward understanding the risks associated with these technologies and provides a foundation for future research to develop more secure and privacy-respecting VAs.

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