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Balancing Innovation and Privacy: A Red Teaming Approach to Evaluating Phone-Based Large Language Models under AI Privacy Regulations Mangesh Pujari; Anil Kumar Pakina; Anshul Goel
International Journal Science and Technology Vol. 2 No. 3 (2023): November: International Journal Science and Technology
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/ijst.v2i3.1956

The rapid deployment of large language models (LLMs) on mobile devices has introduced significant privacy concerns, particularly regarding data collection, user profiling, and compliance with evolving AI regulations such as the GDPR and the AI Act. While these on-device LLMs promise improved latency and user experience, their potential to inadvertently leak sensitive information remains understudied. This paper proposes a red teaming framework to systematically assess the privacy risks of phone-based LLMs, simulating adversarial attacks to identify vulnerabilities in model behavior, data storage, and inference processes. We evaluate popular mobile LLMs under scenarios such as prompt injection, side-channel exploitation, and unintended memorization, measuring their compliance with strict privacy-by-design principles. Our findings reveal critical gaps in current safeguards, including susceptibility to context-aware deanonymization and insufficient data minimization. We further discuss regulatory implications, advocating for adaptive red teaming as a mandatory evaluation step in AI governance. By integrating adversarial testing into the development lifecycle, stakeholders can preemptively align phone-based AI systems with legal and ethical privacy standards while maintaining functional utility.

Enhancing Cybersecurity in Edge AI through Model Distillation and Quantization: A Robust and Efficient Approach Mangesh Pujari; Anshul Goel; Ashwin Sharma
International Journal Science and Technology Vol. 1 No. 3 (2022): November: International Journal Science and Technology
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/ijst.v1i3.1957

The rapid proliferation of Edge AI has introduced significant cybersecurity challenges, including adversarial attacks, model theft, and data privacy concerns. Traditional deep learning models deployed on edge devices often suffer from high computational complexity and memory requirements, making them vulnerable to exploitation. This paper explores the integration of model distillation and quantization techniques to enhance the security and efficiency of Edge AI systems. Model distillation reduces model complexity by transferring knowledge from a large, cumbersome model (teacher) to a compact, efficient one (student), thereby improving resilience against adversarial manipulations. Quantization further optimizes the student model by reducing bit precision, minimizing attack surfaces while maintaining performance. We present a comprehensive analysis of how these techniques mitigate cybersecurity threats such as model inversion, membership inference, and evasion attacks. Additionally, we evaluate trade-offs between model accuracy, latency, and robustness in resource-constrained edge environments. Experimental results on benchmark datasets demonstrate that distilled and quantized models achieve comparable accuracy to their full-precision counterparts while significantly reducing vulnerability to cyber threats. Our findings highlight the potential of distillation and quantization as key enablers for secure, lightweight, and high-performance Edge AI deployments.

Efficient TinyML Architectures for On-Device Small Language Models: Privacy-Preserving Inference at the Edge Mangesh Pujari; Anshul Goel; Anil Kumar Pakina
International Journal Science and Technology Vol. 3 No. 3 (2024): November: International Journal Science and Technology
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/ijst.v3i3.1958

Deploying small language models (SLMs) on ultra-low-power edge devices requires careful optimization to meet strict memory, latency, and energy constraints while preserving privacy. This paper presents a systematic approach to adapting SLMs for Tiny ML, focusing on model compression, hardware-aware quantization, and lightweight privacy mechanisms. We introduce a sparse ternary quantization technique that reduces model size by 5.8× with minimal accuracy loss and an efficient federated fine-tuning method for edge deployment. To address privacy concerns, we implement on-device differential noise injection during text preprocessing, adding negligible computational overhead. Evaluations on constrained devices (Cortex-M7 and ESP32) show our optimized models achieve 92% of the accuracy of full-precision baselines while operating within 256KB RAM and reducing inference latency by 4.3×. The proposed techniques enable new applications for SLMs in always-on edge scenarios where both efficiency and data protection are critical.

Ethical and Responsible AI: Governance Frameworks and Policy Implications for Multi-Agent Systems Tejaskumar Pujari; Anshul Goel; Ashwin Sharma
International Journal Science and Technology Vol. 3 No. 1 (2024): March: International Journal Science and Technology
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/ijst.v3i1.1962

Semi-autonomous, augmented- Artificial Intelligence has become increasingly relevant as collective activities are practiced by two or more autonomic entities. MAS and AI at the intersection have fostered very new waves of socioeconomic exchange, necessitating technological governance and, the most challenging element of them all, ethical governance. These autonomous systems involve a network of decision-making agents working in a decentralized environment, entailing very high accountability, transparency, explanability, ethical alignment, and practically everything in between. The escalated societal functioning of these systems necessitates massive social governance policy interventions and an interdisciplinary governance framework. As an overarching look of multispecialty fields, the research aimed to underscore and pinpoint technology like responsible AI, normative governance frameworks, and multi-agent coordination. This paper unravels insofar as the ethical dilemmas in MAS, picking up loose threads from such international governance configurations and proposing a more adaptive regulatory ethic from an awareness of what it means to coordinate intelligent agents. Bringing together thoughts from ethics, law, computer science, and policy studies, the paper essentially sketches out a path for establishing an AI environment that is sustainable, trustworthy, and ethically grounded.

Ethical and Responsible AI in the Age of Adversarial Diffusion Models: Challenges, Risks, and Mitigation Strategies Tejaskumar Pujari; Anshul Goel; Deepak Kejriwal
International Journal Science and Technology Vol. 1 No. 3 (2022): November: International Journal Science and Technology
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/ijst.v1i3.1963

The rapid pace of diffusion models in generative AI has completely restructured many fields, particularly with respect to image synthesis, video generation, and creative data enhancement. However, promising developments remain tinged with ethical questions in view of diffusion-based model dual-use. By misusing these models, purveyors could think up deepfaked videos, unpredictable forms of misinformation, instead outing cyber warfare-related attacks over the Internet, therefore aggravating societal vulnerabilities. This paper explores and analyzes these potential ethical risks and adversarial threats of diffusion-based artificial intelligence technologies. We lay out the basis for good AI-the notion of fair, accountable, transparent, and robust (FATR) systems-discussing efforts underway to mitigate these ethical risks through watermarking, model alignment, and regulatory mechanisms. Thus, from the dialogue with ethical viewpoints, also touching upon cybersecurity, military policy, or governance, we present a conceptual model to encapsulate probable ethical considerations in the development and deployment of diffusion models. Human-centered values need to be advanced by a proactive convergent bonding among researchers, decision-makers, and civil society players during the strengthening of a tributary of generative AI's power.

Ensuring Responsible AI: The Role of Supervised Fine-Tuning (SFT) in Upholding Integrity and Privacy Regulations Tejaskumar Pujari; Anshul Goel; Ashwin Sharma
International Journal Science and Technology Vol. 3 No. 3 (2024): November: International Journal Science and Technology
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/ijst.v3i3.1968

AI is increasingly used in high-stakes fields such as healthcare, finance, education, and public governance, requiring systems that uphold fairness, accountability, transparency, and privacy. This paper highlights the critical role of Supervised Fine-Tuning (SFT) in aligning large AI models with ethical principles and regulatory frameworks like the GDPR and EU AI Act. The interdisciplinary approach combines regulatory analysis, technical research, and case studies. It proposes integrating privacy-preserving techniques—differential privacy, secure multiparty computation, and federated learning—with SFT during deployment. The research also advocates incorporating Human-in-the-Loop (HITL) and Explainable AI (XAI) to ensure ongoing oversight and interpretability. SFT is positioned not only as a technical method but as a core enabler of responsible AI governance and public trust.

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