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8,837 results · page 8 of 369
Badiea Abdulkarem Mohammed, Mahmood A. Al-Shareeda, Selvakumar Manickam, Zeyad Ghaleb Al-Mekhlafi · 7 authors
No abstract is available for this record.
Abdul Hameed Mohammed
The convergence of the Internet of Things and edge computing represents a fundamental transformation in distributed computing architecture. Traditional cloud-centric models introduce latency and connectivity dependencies flawed for time-touchy packages. Side computing addresses such constraints by positioning computational sources at network peripheries. Distributed processing paradigms restructure data pipelines through intermediate layers between endpoint devices and centralized infrastructure. Fog nodes extend cloud capabilities to locations where data originates. Tiered computation models distinguish between device-level processing, gateway computation, and cloud-based analytics. Aspect synthetic intelligence allows deployment of state-of-the-art machine learning models on resource-limited hardware. Neural network compression strategies consisting of quantization and pruning lessen version complexity while keeping accuracy. Fifth-generation wireless networks provide a connectivity fabric essential for distributed deployments. Multi-access edge computing positions processing resources at radio access network edges. Computation offloading transfers tasks from mobile devices to edge servers strategically. Security frameworks address expanded attack surfaces through zero-trust models and blockchain-based identity management. Distributed ledger architectures eliminate centralized credential repositories. Smart contracts automate security policy enforcement across edge networks reliably.
Dalibor Vodenicarski, Gérald Estadieu
No abstract is available for this record.
Muhammad Asad, Aurora Paz-Pérez, F. Sánchez, Carlos Núñez-Gómez · 12 authors
As the limitations of the current cellular network generation have become apparent to tackle current connectivity needs in 6G, the scientific communities have started to investigate novel techniques to bolster the capabilities of the communication infrastructure. Within 6G, Artificial Intelligence (AI) and Distributed Ledger Technology (DLT) are envisioned as key enablers to drive network performance and guarantee process integrity. However, from a security standpoint, those methods are dual-edged as they introduce a new threat surface that could be used to jeopardise the platform security, the trust in service utility and data privacy. This survey provides a consolidated review of the security, trust, and privacy impact of key 6G enabling technologies. The analysis begins by identifying the primary architectural drivers anticipated for next-generation mobile networks, systematically mapping their impact on the threat surface to identify critical resilience challenges. Conversely, we pinpoint protection methodologies enacted by these drivers, outlining concrete countermeasures that enhance the network’s security posture. Finally, we propose a unified reference architecture that integrates these benefits for holistic security, privacy, and trust management, complemented by a system-level evaluation.
Suseta Datta, Rajdeep Roy, Sourav Banerjee, Utpal Biswas
No abstract is available for this record.
Inam Ul Haq, Sanna Mehraj Kak, Mashhud Ul Islam
No abstract is available for this record.
C. M. Nalayini, M. Ahil, A. B. Swetha, P. B. Vaishali Srija
No abstract is available for this record.
Kirti Jainani, Pankaj Jainani
No abstract is available for this record.
Ramesh Kasarla
Federated Learning (FL) has emerged as a transformative paradigm for distributed machine learning, enabling model training across decentralized edge devices while preserving data privacy. This methodology is critical for sectors handling sensitive information, such as finance, healthcare, and the Internet of Things (IoT). Despite its benefits, the coordination and communication overhead between distributed nodes remain significant challenges. This paper evaluates the efficacy of REST and GraphQL API architectures in facilitating FL workflows. While REST APIs are favored for their statelessness and simplicity, GraphQL offers enhanced flexibility and efficiency by enabling precise data fetching—a vital feature for bandwidth-constrained decentralized systems. We provide a comparative analysis of these paradigms across performance, security, and scalability metrics, specifically regarding data synchronization and model aggregation. Finally, we propose design best practices for developing APIs that support robust, compliant, and efficient federated prediction systems.
Dimitrios Kasimatis, William J. Buchanan, Pavlos Papadopoulos, Nikolaos Pitropakis
No abstract is available for this record.
Thi Tam Pham, Phuc Hau Nguyen, Рафит Ренатович Набиев
The rapid growth of cloud computing has enabled flexible data storage and sharing; however, it also introduces significant challenges related to security, privacy, and access control. This paper proposes a blockchain-based secure data sharing framework to address the limitations of traditional cloud architectures. The proposed framework integrates distributed ledger technology with smart contracts to enable automated and transparent authentication and access control mechanisms. Data are stored off-chain, while metadata and access permissions are recorded on the blockchain to ensure integrity and traceability. Mathematical models are developed to evaluate the probability of valid access and the effectiveness of the access control mechanism. Analytical results demonstrate that the proposed approach significantly enhances security, mitigates single point of failure risks, and improves resistance against common attacks. Although the use of blockchain introduces additional latency due to consensus mechanisms, the system maintains high scalability. This study provides an effective and practical solution for secure data sharing in distributed cloud environments.
Mizbah Syed, R. Sreedevi, Rethu Chrishel, M. Rajavel
No abstract is available for this record.
Naveenbalaji Gowthaman, Manoj Shanmugam
No abstract is available for this record.
Abdelhak Kaddari, Tarik Chanyour
No abstract is available for this record.
G.V. Radhakrishnan, Agnieszka Góra-Błaszczykowska
No abstract is available for this record.
S. Usharani, P. Manju Bala, A. Devi, A. Balachandar · 5 authors
No abstract is available for this record.
Saeed Ahmadi
No abstract is available for this record.
Lanyi Wang
In recent years, decentralized networks have increasingly relied on secure and efficient data exchange systems to support large-scale operations and collaborative processes. Traditional centralized systems face challenges in scalability, transparency, and trust management, which blockchain technology can address. However, existing research has primarily focused on data integrity and static trust models, neglecting dynamic trust propagation, privacy concerns, and the interpretability of trust-related decisions. This study proposes a blockchain-driven information system that integrates a Trust Score Aggregation Module (TSAM), a Hybrid Consensus Protocol (HCP), and a Privacy-Preserving Smart Contract Framework (PPSCF) to address these gaps. The TSAM enables dynamic trust propagation, while the HCP optimizes communication efficiency, and the PPSCF ensures privacy through zero-knowledge proofs. Experimental results show that the proposed system reduces latency by 27.3 % (0.98±0.07 s), increases throughput by 27.1 % (140±6 tps), and achieves a 33.3 % reduction in trust variance compared to baseline systems. The system also improves interpretability by 22.0 %, maintaining low privacy overhead (5.1±0.8 %). This research advances the understanding of blockchain-based trust management in decentralized environments, providing a scalable, interpretable, and privacy-preserving framework that can be applied across various domains and operational scales. The proposed methodology lays a foundation for future blockchain applications in large-scale systems, particularly in environments requiring robust data governance and compliance.
Jesús Rosa-Bilbao
The integration of Internet of Things (IoT) infrastructures with Distributed Ledger Technologies (DLT) remains challenging due to the reliance on complex, tightly coupled back-end systems or centralized oracle services that h... | Find, read and cite all the research you need on Tech Science Press
Bahar Jalali Farahani, Farshad Firouzi, Avid Alizadeh, Mahmoud Daneshmand
No abstract is available for this record.
Henry Zhu
This thesis advances privacy-preserving solutions essential for addressing contemporary technological challenges in smart cities, decentralized systems, and algorithmic decision-making processes. Firstly, we introduce a hybrid sensing framework integrating Internet of Things (IoT) sensors and crowdsensing techniques to overcome limitations inherent in traditional methods. The hybrid sensing model incentivizes voluntary user contributions to complement fixed-location IoT sensors, ensuring reliable and comprehensive data collection while maintaining user anonymity through a privacy-preserving protocol. We implement this model in a smart parking application, demonstrating significant improvements in data accuracy and user engagement. Secondly, we propose a decentralized anonymous crowdsourcing system leveraging blockchain technology, which removes reliance on centralized intermediaries, thereby enhancing transparency and mitigating biases. Our system integrates anonymous payments using the Zerocoin protocol framework, eliminating the need for worker identity registration and trusted setups, thus fostering genuinely anonymous participation. Empirical analyses confirm that our approach maintains practical efficiency in transaction verification and moderate blockchain gas costs. Lastly, we tackle fairness and transparency in algorithmic decision-making processes, addressing public concerns regarding inherent biases and opaque computational practices. We develop a privacy-preserving, publicly verifiable framework that combines succinct zero-knowledge proofs with blockchain infrastructure, allowing independent verification of algorithmic fairness without exposing sensitive inputs or decision-making algorithms. Our concrete instantiation employs a restricted KZG polynomial commitment scheme alongside the Sonic zk-SNARK protocol, demonstrating small proof sizes, efficient verification, and practical deployment feasibility. Collectively, this thesis contributes significantly to the field by providing robust, scalable, and privacy-conscious technologies tailored for contemporary smart city applications and decentralized computational ecosystems.
Renu Mishra, Mamta Narwaria, Murari Kumar Singh, Vimal Dwivedi · 6 authors
No abstract is available for this record.
Xiao Wei
With the rapid increase of terminal devices in the Internet of Things (IoT), it has become a significant challenge to achieve real-time and privacy-preserving data aggregation. To address this challenge, edge computing has emerged as an effective paradigm to reduce latency, where a privacy-preserving data aggregation scheme is exploited to preserve data privacy. However, most existing privacy-preserving data aggregation schemes are limited by fixed data dimensions, low scalability, and high communication or computational overhead. To address these shortcomings, this paper proposes a multidimensional privacy-preserving data aggregation scheme that supports flexible dimension expansion and privacy protection in edge computing systems. The scheme integrates the Chinese Remainder Theorem (CRT) with an elastic modulus set to efficiently pack multidimensional data. This design enables terminal devices to add new data dimensions without interrupting current operations or modifying historical data. Furthermore, by exploiting Bulletproofs-based zero-knowledge proofs and Bellare-Neven (BN) signatures with half-aggregation, the proposed scheme enables lightweight and scalable batch verification of data integrity and authenticity. These mechanisms effectively reduce the verification workload and communication bandwidth in large-scale deployments. In addition, an optimized Paillier homomorphic encryption algorithm is used to enable efficient aggregation of encrypted multidimensional data. Experimental results and theoretical analysis show that the proposed scheme significantly reduces computational and communication costs compared with existing methods.
Tyler Nicewarner, Ali Allami, Dan Lin
Ensuring efficient task assignment and secure payment in mobile crowdsensing while preserving worker location privacy remains a challenging problem. Existing solutions either rely on expensive encryption schemes, employ blockchain-based verification that incurs high computational and gas costs, or use differential privacy techniques that degrade spatial accuracy. This paper introduces the Privacy-preserving Task Assignment and Payment (PTAP) framework, a lightweight solution built upon secure multi-party computation (SMPC). PTAP employs additive secret sharing and a challenge-response mechanism across three semi-honest servers to achieve anonymous task allocation and payment without blockchain or zero-knowledge proofs. The framework guarantees full unlinkability between worker identities, task locations, and payment records while maintaining accurate location-based assignment and supporting traceability for dispute resolution. Experimental evaluation using the MP-SPDZ framework demonstrates scalability to over 1.5 million workers and 7 million payment tokens. The average end-to-end completion time is approximately 35.4 seconds, with zero gas cost. Compared to the state-of-the-art AVeCQ system [15], which requires about 13 minutes and 37 MWei per transaction on the Goerli network for only 1,024 users. The results confirm PTAP's efficiency, scalability, and strong privacy guarantees for large-scale mobile crowdsensing deployments.