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J. Bakal

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Conference Jul 2026

TrustIoT-Chain: A Privacy-Preserving Sharded Blockchain Framework with Zero-Knowledge Auditing for Smart City IoT Monitoring

Smart cities increasingly depend on large-scale Internet of Things (IoT) infrastructures for traffic management, smart grids, and environmental monitoring. Ensuring data integrity, transparency, and privacy in such systems remains a major challenge because centralized platforms are vulnerable to manipulation, while conventional blockchain-based solutions suffer from scalability and confidentiality limitations. This study proposes TrustIoT-Chain, a privacy-preserving blockchain framework that integrates off-chain digital twins, cryptographic data commitments, zero-knowledge compliance verification, and a sharded blockchain architecture for scalable smart city monitoring. The objective of this work is to provide real-time verifiable IoT monitoring with strong privacy guarantees and high system throughput. Large-scale simulations with one million synthetic IoT events demonstrate that the proposed framework achieves up to 24,910 events/s throughput with an average verification latency of 410 ms using 16 shards. Energy consumption is reduced by approximately 45% compared with non-sharded blockchain systems with zeroknowledge proofs, while privacy leakage measured by mutual information decreases to 0.05 bits. The key novelty lies in the joint integration of the digital twins with the blockchain-based zero-knowledge auditing, and sharding for the smart city IoT systems. This approach enables transparent regulatory compliance verification without exposing the raw sensor data, offering the scalable, and privacy-aware foundation for the future smart city governance, and trusted IoT ecosystems.

Shrutika Khobragade, J. Bakal · 0 citations
Conference Jul 2026

Review of Cyber Threat Detection Techniques in Cloud Computing Environment

Cloud is the essential component for modern computer systems, offering businesses flexible scalability and on-demand resources. However, as attackers use more complex techniques to compromise cloud networks, this technological advancement has ushered in a new era of cybersecurity challenges. Wide-ranging effects, such as data loss, financial penalties, reputational harm, and legal responsibilities, can result from such breaches. In response to these challenges, a strong security framework is essential to effectively protect cloud infrastructure. Recently, several artificial intelligence (AI) techniques have been developed for cyber threat detection. Hence, to get deeper insight into this, the survey aims to analyse the role of cyber threat detection techniques and provide an overview of their applications. To achieve this, around 28 research papers from the years 2023-2026 are reviewed based on their methods, algorithms, datasets, performance metrics, and achievements. Furthermore, this work reviews different types of threats affecting the availability, confidentiality, and integrity of cloud services and resources, and examines the applications, including intrusion detection in cloud and several types of cyber threat detection systems. The core insights formulated in this review provide a comparison of analytics as well as future directions.

Pradnya Patil, J. Bakal · 0 citations