Secure provenance in Wireless Sensor Networks - a survey of provenance schemes

Hameed, Khizar; Haseeb, Junaid; Tayyab, Muhammad; Junaid, Muhammad; Maqsood, Talal Bin; Naqvi, Mustahsan Hammad

doi:10.1109/c-code.2017.7918893

Cited by 14 publications

(10 citation statements)

References 21 publications

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“…In the rest of this section, we present simulation results to show that allocating variable size Bloom filters is better for the SSMP protocol when the number of neighbours reported by the nodes are different, i.e., γ i = γ j , for some i = j. To present these results, we consider a sparse vehicular network consisting of n = 8 nodes such that the neighbour distribution reported by the nodes is given by Γ = [5,3,4,1,4,2,4]. For this network, first, we fix a value of m sum = 280, and then obtain the FPRs for the case of equal sized Bloom filters, i.e., when m i = 40 for each 1 ≤ i ≤ 7.…”

Section: Simulation Results On Parameter Optimizationmentioning

confidence: 99%

“…, k n−1 } are fixed. For each node sx in the sorted list, we pick the last n − γ sx − 2 distinct nodes (excluding node sx) of N sorted , and use the corresponding f f p values when computing the counterparts of node sx in (5). By denoting this set of n − γ sx − 2 nodes by S x , an upper bound on the FPRs can be written as…”

Section: Discussionmentioning

confidence: 99%

“…This is because any packet in the network is vulnerable to security threats, which in turn may lead to catastrophic consequences [4]. A standard way to detect security threats is to use a fixed portion of the packet to carry provenance information of the packet from its origin to the destination [5]. This way, upon receiving the packet, the control center will recover the identity of the processes that modified the packet, and then detect any security breach that might have occurred on the packet.…”

Section: Introductionmentioning

confidence: 99%

“…a network setting with Γ =[5,3,4,1,4,2,4] and msum = 280, we introduced a fixed size Bloom filter for all nodes i.e., m i = 40, and compared that with variable sized Bloom filter given by m = [48,48, 48,16, 48,32, 48] with number of hashes as k =[8,10,8,7,8,9,8]. The plots show that variable sized Bloom filters outperform the equal sized case.…”

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confidence: 99%

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Secure and Ultra-Reliable Provenance Recovery in Sparse Networks: Strategies and Performance Bounds

Sajeev¹,

Bansal²,

Sriraam³

et al. 2022

Preprint

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Provenance embedding algorithms are well known for tracking the footprints of information flow in wireless networks. Recently, low-latency provenance embedding algorithms have received traction in vehicular networks owing to strict deadlines on the delivery of packets. While existing low-latency provenance embedding methods focus on reducing the packet delay, they assume a complete graph on the underlying topology due to the mobility of the participating nodes. We identify that the complete graph assumption leads to sub-optimal performance in provenance recovery, especially when the vehicular network is sparse, which is usually observed outside peak-hour traffic conditions. As a result, we propose a two-part approach to design provenance embedding algorithms for sparse vehicular networks. In the first part, we propose secure and practical topology-learning strategies, whereas in the second part, we design provenance embedding algorithms that guarantee ultra-reliability by incorporating the topology knowledge at the destination during the provenance recovery process. Besides the novel idea of using topology knowledge for provenance recovery, a distinguishing feature for achieving ultra-reliability is the use of hash-chains in the packet, which trade communication-overhead of the packet with the complexity-overhead at the destination. We derive tight upper bounds on the performance of our strategies, and show that the derived bounds, when optimized with appropriate constraints, deliver design parameters that outperform existing methods. Finally, we also implement our ideas on OMNeT++ based simulation environment to show that their latency benefits indeed make them suitable for vehicular network applications.

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Section: Simulation Results On Parameter Optimizationmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Secure and Ultra-Reliable Provenance Recovery in Sparse Networks: Strategies and Performance Bounds

Sajeev¹,

Bansal²,

Sriraam³

et al. 2022

Preprint

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“… Interoperability Standards: More work is needed to establish interoperability standards and frameworks that facilitate seamless integration of blockchain-powered healthcare systems with existing infrastructure. Developing common data formats, standard protocols, and governance models can promote interoperability [ 7 ]. Hybrid Deep Learning Models: Future research should explore advanced hybrid deep learning models specifically designed for healthcare data analysis within a blockchain framework.…”

Section: Related Studiesmentioning

confidence: 99%

Blockchain-Powered Healthcare Systems: Enhancing Scalability and Security with Hybrid Deep Learning

Ali,

Saeed

et al. 2023

Sensors

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The rapid advancements in technology have paved the way for innovative solutions in the healthcare domain, aiming to improve scalability and security while enhancing patient care. This abstract introduces a cutting-edge approach, leveraging blockchain technology and hybrid deep learning techniques to revolutionize healthcare systems. Blockchain technology provides a decentralized and transparent framework, enabling secure data storage, sharing, and access control. By integrating blockchain into healthcare systems, data integrity, privacy, and interoperability can be ensured while eliminating the reliance on centralized authorities. In conjunction with blockchain, hybrid deep learning techniques offer powerful capabilities for data analysis and decision making in healthcare. Combining the strengths of deep learning algorithms with traditional machine learning approaches, hybrid deep learning enables accurate and efficient processing of complex healthcare data, including medical records, images, and sensor data. This research proposes a permissions-based blockchain framework for scalable and secure healthcare systems, integrating hybrid deep learning models. The framework ensures that only authorized entities can access and modify sensitive health information, preserving patient privacy while facilitating seamless data sharing and collaboration among healthcare providers. Additionally, the hybrid deep learning models enable real-time analysis of large-scale healthcare data, facilitating timely diagnosis, treatment recommendations, and disease prediction. The integration of blockchain and hybrid deep learning presents numerous benefits, including enhanced scalability, improved security, interoperability, and informed decision making in healthcare systems. However, challenges such as computational complexity, regulatory compliance, and ethical considerations need to be addressed for successful implementation. By harnessing the potential of blockchain and hybrid deep learning, healthcare systems can overcome traditional limitations, promoting efficient and secure data management, personalized patient care, and advancements in medical research. The proposed framework lays the foundation for a future healthcare ecosystem that prioritizes scalability, security, and improved patient outcomes.

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