Packet Scheduling with Optional Client Privacy

Beams, Andrew; Kannan, Sampath; Angel, Sebastian

doi:10.1145/3460120.3485371

Cited by 3 publications

(2 citation statements)

References 62 publications

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“…Angel et al [26] proposed a new resource allocation scheme that allocates resources to a group of clients and does not disclose to the clients whether there are other clients receiving the resources. Beams et al [10] proposed a privacypreserving packet scheduling scheme that prevents a malicious adversary from obtaining the victim's private information by launching a side-channel attack and observing how the switch schedules packets. Ahmad et al [27] proposed a new voice communication scheme that enables metadata privacy-preserving voice communication over a completely untrusted infrastructure.…”

Section: Related Workmentioning

confidence: 99%

“…The metadata of edge computing contain sensitive information, such as when a user publishes a task, what kind of resource they request, and how much computation resource they need. Unprotected metadata will expose the end device's private information; e.g., a malicious adversary can launch a side-channel attack by observing how the gateway performs the resource allocation to obtain the user's privacy information [10]. Some research has shown that it is possible to get a detailed and private picture of a person's contacts and interests just by analyzing metadata, and it is technically much more accessible to search massive amounts of metadata than to listen in on millions of phone calls [11].…”

Section: Introductionmentioning

confidence: 99%

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Metadata-Private Resource Allocation in Edge Computing Withstands Semi-Malicious Edge Nodes

Zhang,

Li,

et al. 2024

Sensors

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Edge computing provides higher computational power and lower transmission latency by offloading tasks to nearby edge nodes with available computational resources to meet the requirements of time-sensitive tasks and computationally complex tasks. Resource allocation schemes are essential to this process. To allocate resources effectively, it is necessary to attach metadata to a task to indicate what kind of resources are needed and how many computation resources are required. However, these metadata are sensitive and can be exposed to eavesdroppers, which can lead to privacy breaches. In addition, edge nodes are vulnerable to corruption because of their limited cybersecurity defenses. Attackers can easily obtain end-device privacy through unprotected metadata or corrupted edge nodes. To address this problem, we propose a metadata privacy resource allocation scheme that uses searchable encryption to protect metadata privacy and zero-knowledge proofs to resist semi-malicious edge nodes. We have formally proven that our proposed scheme satisfies the required security concepts and experimentally demonstrated the effectiveness of the scheme.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Metadata-Private Resource Allocation in Edge Computing Withstands Semi-Malicious Edge Nodes

Zhang,

Li,

et al. 2024

Sensors

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Real-Time data packet scheduling in WSNs: An Overview

Abdullah

Ahmed

Ghazal

et al. 2022

2022 8th International Conference on Contemporary Information Technology and Mathematics (ICCITM)

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Swarm intelligence-based packet scheduling for future intelligent networks

Husen,

Hasanain Chaudary,

Ahmad

et al. 2023

PeerJ Computer Science

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Network operations involve several decision-making tasks. Some of these tasks are related to operators, such as extending the footprint or upgrading the network capacity. Other decision tasks are related to network functions, such as traffic classifications, scheduling, capacity, coverage trade-offs, and policy enforcement. These decisions are often decentralized, and each network node makes its own decisions based on the preconfigured rules or policies. To ensure effectiveness, it is essential that planning and functional decisions are in harmony. However, human intervention-based decisions are subject to high costs, delays, and mistakes. On the other hand, machine learning has been used in different fields of life to automate decision processes intelligently. Similarly, future intelligent networks are also expected to see an intense use of machine learning and artificial intelligence techniques for functional and operational automation. This article investigates the current state-of-the-art methods for packet scheduling and related decision processes. Furthermore, it proposes a machine learning-based approach for packet scheduling for agile and cost-effective networks to address various issues and challenges. The analysis of the experimental results shows that the proposed deep learning-based approach can successfully address the challenges without compromising the network performance. For example, it has been seen that with mean absolute error from 6.38 to 8.41 using the proposed deep learning model, the packet scheduling can maintain 99.95% throughput, 99.97% delay, and 99.94% jitter, which are much better as compared to the statically configured traffic profiles.

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Packet Scheduling with Optional Client Privacy

Cited by 3 publications

References 62 publications

Metadata-Private Resource Allocation in Edge Computing Withstands Semi-Malicious Edge Nodes

Metadata-Private Resource Allocation in Edge Computing Withstands Semi-Malicious Edge Nodes

Real-Time data packet scheduling in WSNs: An Overview

Swarm intelligence-based packet scheduling for future intelligent networks

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