arXiv subjects

Dhiman Goswami

Publications and source records attributed to Dhiman Goswami.

At least 19 recordsLinked to original sources

arXiv2026

Teaching Usable Privacy in HCI Education: Designing, Implementing, and Evaluating an Active Learning Graduate Course

As digital systems increasingly rely on pervasive data collection and inference, educating future designers and researchers about Usable Privacy has become a critical need for HCI. However, privacy education in higher education is often fragmented, theory-heavy, or detached from real-world applications. Thus, in this paper, we present the design, implementation, and evaluation of a 15-week graduate-level course on Usable Privacy that addresses this through active, practice-oriented pedagogy. The course integrates use cases, structured role playing, case-based discussions, guest lectures, and a multi-phase research project to support students in reasoning about privacy from multiple stakeholder perspectives. Grounded in contemporary privacy research and the Modern Privacy framework, the curriculum emphasizes both conceptual understanding and applied research skills. We report findings from two course offerings in consecutive years (2024-2025) using a mixed-methods evaluation that combines quantitative teaching evaluations with qualitative analysis of student reflections and instructor observations. Results indicate increased student engagement, improved ability to articulate trade-offs in privacy design, and stronger connections between theory and practice. To support adoption and replication, we also release detailed assignment descriptions and grading rubrics. This work contributes an empirically informed model for teaching Usable Privacy in HCI education and offers actionable guidance for educators seeking to integrate privacy into their curricula.

cs.CY↗

arXiv2026

SoK: Analysis of Privacy Risks and Mitigation in Online Propaganda Detection through the PROMPT Framework

Online propaganda detection pipelines expose measurable privacy risks at multiple stages including data collection, feature extraction, and model inference. We conduct a structured analysis of $162$ peer-reviewed studies and formalize the problem using the Propaganda Risk Online Mitigation and Privacy-preserving Tactics (PROMPT) framework. PROMPT models risks $R$ and mitigation strategies $S$ through a mapping $M: R\to S$ guided by a utility function $α\cdot \mathrm{PrivacyGain}(s_j) - β\cdot \mathrm{PerfLoss}(s_j) - γ\cdot \mathrm{Cost}(s_j)$, with tunable $(α,β,γ)$ enabling stakeholders to balance privacy, accuracy, and deployment costs. To assess practical adoption, we introduce a compliance score that quantifies the alignment of existing methods with GDPR, CCPA etc. requirements. Our evaluation shows that many widely used pipelines remain non-compliant, particularly in metadata handling and user-level aggregation. We further present empirical fine-tuning experiments on transformer-based encoders and decoders under synthetic perturbation, demonstrating a monotonic privacy-utility trade-off: with $q = 0.05$ performance decreased by 1-2% F$_1$, while at $q = 0.20$ the reduction reached 13-14%. These results establish quantitative baselines for privacy costs in propaganda detection. Our contributions include a formal risk-to-defense mapping, a compliance-oriented auditing metric, and experimental evidence of privacy-performance trade-offs, providing a technical foundation for building regulation-compliant and privacy-aware detection systems.

cs.CR↗

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

NLP Privacy Risk Identification in Social Media (NLP-PRISM): A Survey

Natural Language Processing (NLP) is integral to social media analytics but often processes content containing Personally Identifiable Information (PII), behavioral cues, and metadata raising privacy risks such as surveillance, profiling, and targeted advertising. To systematically assess these risks, we review 203 peer-reviewed papers and propose the NLP Privacy Risk Identification in Social Media (NLP-PRISM) framework, which evaluates vulnerabilities across six dimensions: data collection, preprocessing, visibility, fairness, computational risk, and regulatory compliance. Our analysis shows that transformer models achieve F1-scores ranging from 0.58-0.84, but incur a 1% - 23% drop under privacy-preserving fine-tuning. Using NLP-PRISM, we examine privacy coverage in six NLP tasks: sentiment analysis (16), emotion detection (14), offensive language identification (19), code-mixed processing (39), native language identification (29), and dialect detection (24) revealing substantial gaps in privacy research. We further found a (reduced by 2% - 9%) trade-off in model utility, MIA AUC (membership inference attacks) 0.81, AIA accuracy 0.75 (attribute inference attacks). Finally, we advocate for stronger anonymization, privacy-aware learning, and fairness-driven training to enable ethical NLP in social media contexts.

cs.CL↗

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↗

arXiv2024

MasonTigers at SemEval-2024 Task 10: Emotion Discovery and Flip Reasoning in Conversation with Ensemble of Transformers and Prompting

In this paper, we present MasonTigers' participation in SemEval-2024 Task 10, a shared task aimed at identifying emotions and understanding the rationale behind their flips within monolingual English and Hindi-English code-mixed dialogues. This task comprises three distinct subtasks - emotion recognition in conversation for Hindi-English code-mixed dialogues, emotion flip reasoning for Hindi-English code-mixed dialogues, and emotion flip reasoning for English dialogues. Our team, MasonTigers, contributed to each subtask, focusing on developing methods for accurate emotion recognition and reasoning. By leveraging our approaches, we attained impressive F1-scores of 0.78 for the first task and 0.79 for both the second and third tasks. This performance not only underscores the effectiveness of our methods across different aspects of the task but also secured us the top rank in the first and third subtasks, and the 2nd rank in the second subtask. Through extensive experimentation and analysis, we provide insights into our system's performance and contributions to each subtask.

cs.CL↗

arXiv2024

EmoMix-3L: A Code-Mixed Dataset for Bangla-English-Hindi Emotion Detection

Code-mixing is a well-studied linguistic phenomenon that occurs when two or more languages are mixed in text or speech. Several studies have been conducted on building datasets and performing downstream NLP tasks on code-mixed data. Although it is not uncommon to observe code-mixing of three or more languages, most available datasets in this domain contain code-mixed data from only two languages. In this paper, we introduce EmoMix-3L, a novel multi-label emotion detection dataset containing code-mixed data from three different languages. We experiment with several models on EmoMix-3L and we report that MuRIL outperforms other models on this dataset.

cs.CL↗

arXiv2024

MasonTigers at SemEval-2024 Task 1: An Ensemble Approach for Semantic Textual Relatedness

This paper presents the MasonTigers entry to the SemEval-2024 Task 1 - Semantic Textual Relatedness. The task encompasses supervised (Track A), unsupervised (Track B), and cross-lingual (Track C) approaches across 14 different languages. MasonTigers stands out as one of the two teams who participated in all languages across the three tracks. Our approaches achieved rankings ranging from 11th to 21st in Track A, from 1st to 8th in Track B, and from 5th to 12th in Track C. Adhering to the task-specific constraints, our best performing approaches utilize ensemble of statistical machine learning approaches combined with language-specific BERT based models and sentence transformers.

cs.CL↗

arXiv2024

MasonTigers at SemEval-2024 Task 8: Performance Analysis of Transformer-based Models on Machine-Generated Text Detection

This paper presents the MasonTigers entry to the SemEval-2024 Task 8 - Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection. The task encompasses Binary Human-Written vs. Machine-Generated Text Classification (Track A), Multi-Way Machine-Generated Text Classification (Track B), and Human-Machine Mixed Text Detection (Track C). Our best performing approaches utilize mainly the ensemble of discriminator transformer models along with sentence transformer and statistical machine learning approaches in specific cases. Moreover, zero-shot prompting and fine-tuning of FLAN-T5 are used for Track A and B.

cs.CL↗

arXiv2024

CSEPrompts: A Benchmark of Introductory Computer Science Prompts

Recent advances in AI, machine learning, and NLP have led to the development of a new generation of Large Language Models (LLMs) that are trained on massive amounts of data and often have trillions of parameters. Commercial applications (e.g., ChatGPT) have made this technology available to the general public, thus making it possible to use LLMs to produce high-quality texts for academic and professional purposes. Schools and universities are aware of the increasing use of AI-generated content by students and they have been researching the impact of this new technology and its potential misuse. Educational programs in Computer Science (CS) and related fields are particularly affected because LLMs are also capable of generating programming code in various programming languages. To help understand the potential impact of publicly available LLMs in CS education, we introduce CSEPrompts, a framework with hundreds of programming exercise prompts and multiple-choice questions retrieved from introductory CS and programming courses. We also provide experimental results on CSEPrompts to evaluate the performance of several LLMs with respect to generating Python code and answering basic computer science and programming questions.

cs.CL↗

arXiv2024

MasonTigers at SemEval-2024 Task 9: Solving Puzzles with an Ensemble of Chain-of-Thoughts

Our paper presents team MasonTigers submission to the SemEval-2024 Task 9 - which provides a dataset of puzzles for testing natural language understanding. We employ large language models (LLMs) to solve this task through several prompting techniques. Zero-shot and few-shot prompting generate reasonably good results when tested with proprietary LLMs, compared to the open-source models. We obtain further improved results with chain-of-thought prompting, an iterative prompting method that breaks down the reasoning process step-by-step. We obtain our best results by utilizing an ensemble of chain-of-thought prompts, placing 2nd in the word puzzle subtask and 13th in the sentence puzzle subtask. The strong performance of prompted LLMs demonstrates their capability for complex reasoning when provided with a decomposition of the thought process. Our work sheds light on how step-wise explanatory prompts can unlock more of the knowledge encoded in the parameters of large models.

cs.CL↗

arXiv2024

Mixed-Distil-BERT: Code-mixed Language Modeling for Bangla, English, and Hindi

One of the most popular downstream tasks in the field of Natural Language Processing is text classification. Text classification tasks have become more daunting when the texts are code-mixed. Though they are not exposed to such text during pre-training, different BERT models have demonstrated success in tackling Code-Mixed NLP challenges. Again, in order to enhance their performance, Code-Mixed NLP models have depended on combining synthetic data with real-world data. It is crucial to understand how the BERT models' performance is impacted when they are pretrained using corresponding code-mixed languages. In this paper, we introduce Tri-Distil-BERT, a multilingual model pre-trained on Bangla, English, and Hindi, and Mixed-Distil-BERT, a model fine-tuned on code-mixed data. Both models are evaluated across multiple NLP tasks and demonstrate competitive performance against larger models like mBERT and XLM-R. Our two-tiered pre-training approach offers efficient alternatives for multilingual and code-mixed language understanding, contributing to advancements in the field.

cs.CL↗

arXiv2024

qPMS Sigma -- An Efficient and Exact Parallel Algorithm for the Planted $(l, d)$ Motif Search Problem

Motif finding is an important step for the detection of rare events occurring in a set of DNA or protein sequences. Extraction of information about these rare events can lead to new biological discoveries. Motifs are some important patterns that have numerous applications including the identification of transcription factors and their binding sites, composite regulatory patterns, similarity between families of proteins, etc. Although several flavors of motif searching algorithms have been studied in the literature, we study the version known as $ (l, d) $-motif search or Planted Motif Search (PMS). In PMS, given two integers $ l $, $ d $ and $ n $ input sequences we try to find all the patterns of length $ l $ that appear in each of the $ n $ input sequences with at most $ d $ mismatches. We also discuss the quorum version of PMS in our work that finds motifs that are not planted in all the input sequences but at least in $ q $ of the sequences. Our algorithm is mainly based on the algorithms qPMSPrune, qPMS7, TraverStringRef and PMS8. We introduce some techniques to compress the input strings and make faster comparison between strings with bitwise operations. Our algorithm performs a little better than the existing exact algorithms to solve the qPMS problem in DNA sequence. We have also proposed an idea for parallel implementation of our algorithm.

cs.DS↗

arXiv2024

MasonPerplexity at Multimodal Hate Speech Event Detection 2024: Hate Speech and Target Detection Using Transformer Ensembles

The automatic identification of offensive language such as hate speech is important to keep discussions civil in online communities. Identifying hate speech in multimodal content is a particularly challenging task because offensiveness can be manifested in either words or images or a juxtaposition of the two. This paper presents the MasonPerplexity submission for the Shared Task on Multimodal Hate Speech Event Detection at CASE 2024 at EACL 2024. The task is divided into two sub-tasks: sub-task A focuses on the identification of hate speech and sub-task B focuses on the identification of targets in text-embedded images during political events. We use an XLM-roBERTa-large model for sub-task A and an ensemble approach combining XLM-roBERTa-base, BERTweet-large, and BERT-base for sub-task B. Our approach obtained 0.8347 F1-score in sub-task A and 0.6741 F1-score in sub-task B ranking 3rd on both sub-tasks.

cs.CL↗

arXiv2024

MasonTigers@LT-EDI-2024: An Ensemble Approach Towards Detecting Homophobia and Transphobia in Social Media Comments

In this paper, we describe our approaches and results for Task 2 of the LT-EDI 2024 Workshop, aimed at detecting homophobia and/or transphobia across ten languages. Our methodologies include monolingual transformers and ensemble methods, capitalizing on the strengths of each to enhance the performance of the models. The ensemble models worked well, placing our team, MasonTigers, in the top five for eight of the ten languages, as measured by the macro F1 score. Our work emphasizes the efficacy of ensemble methods in multilingual scenarios, addressing the complexities of language-specific tasks.

cs.CL↗

arXiv2024

MasonPerplexity at ClimateActivism 2024: Integrating Advanced Ensemble Techniques and Data Augmentation for Climate Activism Stance and Hate Event Identification

The task of identifying public opinions on social media, particularly regarding climate activism and the detection of hate events, has emerged as a critical area of research in our rapidly changing world. With a growing number of people voicing either to support or oppose to climate-related issues - understanding these diverse viewpoints has become increasingly vital. Our team, MasonPerplexity, participates in a significant research initiative focused on this subject. We extensively test various models and methods, discovering that our most effective results are achieved through ensemble modeling, enhanced by data augmentation techniques like back-translation. In the specific components of this research task, our team achieved notable positions, ranking 5th, 1st, and 6th in the respective sub-tasks, thereby illustrating the effectiveness of our approach in this important field of study.

cs.CL↗

arXiv2023

SentMix-3L: A Bangla-English-Hindi Code-Mixed Dataset for Sentiment Analysis

Code-mixing is a well-studied linguistic phenomenon when two or more languages are mixed in text or speech. Several datasets have been build with the goal of training computational models for code-mixing. Although it is very common to observe code-mixing with multiple languages, most datasets available contain code-mixed between only two languages. In this paper, we introduce SentMix-3L, a novel dataset for sentiment analysis containing code-mixed data between three languages Bangla, English, and Hindi. We carry out a comprehensive evaluation using SentMix-3L. We show that zero-shot prompting with GPT-3.5 outperforms all transformer-based models on SentMix-3L.

cs.CL↗

arXiv2023

OffMix-3L: A Novel Code-Mixed Dataset in Bangla-English-Hindi for Offensive Language Identification

Code-mixing is a well-studied linguistic phenomenon when two or more languages are mixed in text or speech. Several works have been conducted on building datasets and performing downstream NLP tasks on code-mixed data. Although it is not uncommon to observe code-mixing of three or more languages, most available datasets in this domain contain code-mixed data from only two languages. In this paper, we introduce OffMix-3L, a novel offensive language identification dataset containing code-mixed data from three different languages. We experiment with several models on this dataset and observe that BanglishBERT outperforms other transformer-based models and GPT-3.5.

cs.CL↗