Retrieval-augmented techniques improve automated code repair by grounding generation in external programming knowledge, but most prior approaches assume a static knowledge base. This assumption is problematic in scientific computing, where libraries, APIs, and dependencies evolve continuously. This paper presents RAILS-DK, a dynamic knowledge management framework for retrieval-augmented code repair that treats programming knowledge as an explicit, evolving artifact. RAILS-DK uses a topic-driven registry to control domain-specific APIs and deterministically regenerate documentation and retrieval indices, ensuring verified, environment-aligned, and reproducible repair. We evaluate RAILS-DK on two benchmarks comprising over six hundred compilation- and execution-validated repair instances across Java and Python. The first extends a Java import-resolution benchmark with a synthetic scientific API to isolate knowledge effects from pretrained memorization; the second evaluates execution-level import failures in representative Python scientific kernels. Across both settings, prompt-only and static retrieval fail on unseen or evolving APIs, while RAILS-DK consistently retrieves correct bindings and achieves successful repair, typically within two iterations. These results show that dynamic knowledge management is critical for reliable retrievalaugmented code repair in scientific computing workflows.
Wali Mohammad Abdullah, Sayeed Islam, Md. Morshedul Islam et al.· Annual International Compute...· 0 citations
Document fraud, particularly involving passports, poses significant security risks and can lead to severe consequences such as unauthorized entries into a country. Blockchain technology has advanced the concept of Self-Sovereign Identity (SSI), a user-controlled and secure identity management paradigm built on top of blockchain technology. This paper proposes a two-layer blockchain-based SSI digital passport system. The first layer establishes a trusted consortium of countries that serves as a decentralized trust anchor, enabling governance, interoperability, and secure cross-border collaboration analogous to existing international passport verification frameworks. The second layer builds on this foundation to implement an SSIbased digital passport using Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs), allowing users to carry and verify their passports digitally while maintaining privacy through selective disclosure. We present the architecture and detailed protocol of the proposed system, analyze security and privacy threats, and describe how these threats mitigated through concrete design requirements. Finally, a Proof-of-Concept (PoC) implementation using state-of-the-art technologies demonstrates the system's feasibility and evaluates its performance.
Sifat Jahan Sajin, Mohammad Mahmudul Haque Siam, Abdullah Al Anan et al.· Annual International Compute...· 0 citations
Large Language Models (LLMs) offer significant potential for clinical decision support, but their deployment is constrained by hallucination, privacy risks, and limited interpretability. Retrieval-Augmented Generation (RAG) improves reliability by grounding outputs in patient-specific electronic health records (EHRs); however, it introduces new challenges related to sensitive data exposure. This paper proposes a governanceaware, privacy-preserving RAG framework for clinical triage decision support using the MIMIC-IV-Ext dataset. The framework integrates retrieval-based grounding, differential privacy, explainability, and an agentic orchestration layer within a unified architecture. An $\epsilon$-ledger is introduced to track cumulative privacy expenditure, and a Privacy-Utility-Explainability (PUE) framework is used to systematically evaluate trade-offs. Experimental results demonstrate that the proposed approach improves classification performance (e.g., achieving up to 82% accuracy and 88% AUROC) and reduces under-triage risk, while privacy mechanisms introduce only modest performance degradation. Retrieval-based attribution further enhances interpretability by linking predictions to clinically relevant cases. These results highlight the effectiveness of combining retrieval, privacy, and explainability for developing trustworthy and deployable healthcare AI systems.
A. K. I. Riad, Md Abdul Barek, Hossain Shahriar et al.· International Conference on...· 0 citations
The rapid growth of the Internet of Things (IoT) in critical domains such as healthcare, smart cities, cybersecurity, and finance has led to the generation of large volumes of distributed data and increased susceptibility to cyberattacks. Although federated learning (FL)-based intrusion detection systems (IDS) have been introduced to support distributed learning and improve privacy, they still face several challenges, including performance limitations, high computation and communication overhead, and potential privacy attacks. To address these challenges, we propose a Random Projection-Based Personalized Federated Learning (RPPFL) framework for IoT intrusion detection. In the proposed framework, random projection, a lightweight one-way dimensionality reduction transformation, is applied at the IoT device level, which enhances privacy while lowering computational and communication costs. Furthermore, personalized federated learning at the fog layer reduces the computational burden on resource-constrained IoT devices and improves model robustness in environments where IoT data are non-independent and identically distributed (non-IID). We also introduce a conditional generative adversarial network (cGAN)-based privacy attack to evaluate the resilience of the proposed framework and demonstrate the effectiveness of our approach in preserving the privacy of IoT data. Experimental results on the RT-IoT 2022 and CIC-IoT 2023 datasets demonstrate that RPPFL provides high detection accuracy (above 95.0%) while preserving data privacy and reducing computation and communication overhead in dynamic IoT environments. The proposed framework is generalizable and applicable to a wide range of IoT intrusion detection scenarios.
Md. Morshedul Islam, Hossain Shahriar, Alfredo Cuzzocrea et al.· Annual International Compute...· 0 citations
Large Language Models (LLMs) are now deployed at an unprecedented scale across many critical sectors, rapidly transitioning from experimental AI tools to embedded components of production software systems. This accelerated adoption, often enabled by low-code integrations, has lowered technical barriers while simultaneously expanding the attack surface of modern applications, particularly when deployments occur without sufficient domain-specific security expertise. In many cases, security maturity has not progressed at the same pace as capability expansion, creating systemic exposure across confidentiality, integrity, and availability dimensions. To provide structured clarity amid this rapid growth, this paper presents a comparative and standards-aligned analysis of LLM security risks and defense mechanisms grounded in the OWASP GenAI Top-10 (2025). We systematically examine each vulnerability class, map representative attack patterns to primary mitigation strategies, evaluate their security property impact, and analyze practical limitations and implementation trade-offs. In addition, we introduce a severity-based assessment to prioritize risks according to operational and systemic impact, offering a quantitative perspective on defensive readiness. Our findings indicate that current mitigation strategies are predominantly reactive, concentrated at inference time, and unevenly distributed across the LLM lifecycle. Controls addressing training pipelines, supplychain dependencies, and autonomous system behaviors remain comparatively less mature and less standardized. By integrating vulnerability classification, defense mapping, severity prioritization, and trade-off analysis within a unified framework, this study provides actionable guidance for strengthening secure, resilient, and standards-driven LLM deployment in high-stakes environments.
Md Abdul Barek, Md Bajlur Rashid, A. K. I. Riad et al.· Annual International Compute...· 0 citations