Physical-Layer Assisted Secure Offloading in Mobile-Edge Computing

He, Xiaofan; Jin, Richeng; Dai, Huaiyu

doi:10.1109/twc.2020.2979456

Cited by 55 publications

(14 citation statements)

References 38 publications

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“…Multiple antennas technologies were also used to improve the performance of secure offloading systems. He et al used multiple antennas of SBS to generate AN signals to impede eavesdropping and leverage full-duplex communication technologies to suppress self-interference [33].…”

Section: A Mec Technologiesmentioning

confidence: 99%

“…Since H me,1 and H me,2 are independent and identically distributed, we simplify H me,1 and H me,2 as a complex Gaussian random matrix H me . In this paper, we emphasize the secure computation offloading approach and assume that channel estimation is perfect, as what have been assumed in [12], [14], [30]- [33]. However, in practice, perfect CSIs may not be available due to channel estimation and feedback errors.…”

Section: Channel Modelmentioning

confidence: 99%

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Physical Layer Security Assisted Computation Offloading in Intelligently Connected Vehicle Networks

Liu

Wang

Chen

et al. 2021

IEEE Trans. Wireless Commun.

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In this paper, we propose a secure computation offloading scheme (SCOS) in intelligently connected vehicle (ICV) networks, aiming to minimize overall latency of computing via offloading part of computational tasks to nearby servers in small cell base stations (SBSs), while securing the information delivered during offloading and feedback phases via physical layer security. Existing computation offloading schemes usually neglected time-varying characteristics of channels and their corresponding secrecy rates, resulting in an inappropriate task partition ratio and a large secrecy outage probability. To address these issues, we utilize an ergodic secrecy rate to determine how many tasks are offloaded to the edge, where ergodic secrecy rate represents the average secrecy rate over all realizations in a time-varying wireless channel. Adaptive wiretap code rates are proposed with a secrecy outage constraint to match time-varying wireless channels. In addition, the proposed secure beamforming and artificial noise (AN) schemes can improve the ergodic secrecy rates of uplink and downlink channels even without eavesdropper channel state information (CSI). Numerical results demonstrate that the proposed schemes have a shorter system delay than the strategies neglecting time-varying characteristics.

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Section: A Mec Technologiesmentioning

confidence: 99%

Section: Channel Modelmentioning

confidence: 99%

Physical Layer Security Assisted Computation Offloading in Intelligently Connected Vehicle Networks

Liu

Wang

Chen

et al. 2021

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

show abstract

“…Furthermore, we assume that the eavesdropper knows h EVE ; the EN knows perfectly h EN , h H , and G but only knows an estimate ĥEVE of h EVE . Following the deterministic uncertainty model [22], [23], we assume that hEVE− ĥEVE ĥEVE ≤ , where is an upper bound of the relative estimation error.…”

Section: A Communication Modelmentioning

confidence: 99%

Low-Latency and Secure Computation Offloading Assisted by Hybrid Relay-Reflecting Intelligent Surface

Ngo¹,

Nguyen²,

Dinh³

et al. 2021

Preprint

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Recently, the hybrid relay-reflecting intelligent surface (HRRIS) has been introduced as a spectral-and energyefficient architecture to assist wireless communication systems. In the HRRIS, a single or few active relay elements are deployed along with a large number of passive reflecting elements, allowing it to not only reflect but also amplify the incident signals. In this work, we investigate the potential of the HRRIS in aiding the computation offloading in a single-user mobile edge computing system. The objective is to minimize the offloading latency while ensuring the secrecy of user data against a malicious eavesdropper. We develop efficient solutions to this latency minimization problem based on alternating optimization. Through numerical results, we show that the deployment of the HRRIS can result in a considerable reduction in latency. Furthermore, the latency reduction gain offered by the HRRIS is much more significant than that of the conventional reconfigurable intelligent surface (RIS).

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“…Even though physical layer security techniques such as noisy signals and receiver jamming ( [47]) have been utilized to promote the security and privacy of the conventional wireless communication systems [48], there are very few recent works that utilize such techniques. A novel secure offloading scheme based on physical layer has been proposed in Reference [49]. In this work, the edge server broadcasts jamming signals and utilizes full duplex communication to impede the eavesdroppers and reduce the self interference.…”

Section: Related Workmentioning

confidence: 99%

UbiPriSEQ—Deep Reinforcement Learning to Manage Privacy, Security, Energy, and QoS in 5G IoT HetNets

et al. 2020

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5G networks and Internet of Things (IoT) offer a powerful platform for ubiquitous environments with their ubiquitous sensing, high speeds and other benefits. The data, analytics, and other computations need to be optimally moved and placed in these environments, dynamically, such that energy-efficiency and QoS demands are best satisfied. A particular challenge in this context is to preserve privacy and security while delivering quality of service (QoS) and energy-efficiency. Many works have tried to address these challenges but without a focus on optimizing all of them and assuming fixed models of environments and security threats. This paper proposes the UbiPriSEQ framework that uses Deep Reinforcement Learning (DRL) to adaptively, dynamically, and holistically optimize QoS, energy-efficiency, security, and privacy. UbiPriSEQ is built on a three-layered model and comprises two modules. UbiPriSEQ devises policies and makes decisions related to important parameters including local processing and offloading rates for data and computations, radio channel states, transmit power, task priority, and selection of fog nodes for offloading, data migration, and so forth. UbiPriSEQ is implemented in Python over the TensorFlow platform and is evaluated using a real-life application in terms of SINR, privacy metric, latency, and utility function, manifesting great promise.

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Physical-Layer Assisted Secure Offloading in Mobile-Edge Computing

Cited by 55 publications

References 38 publications

Physical Layer Security Assisted Computation Offloading in Intelligently Connected Vehicle Networks

Physical Layer Security Assisted Computation Offloading in Intelligently Connected Vehicle Networks

Low-Latency and Secure Computation Offloading Assisted by Hybrid Relay-Reflecting Intelligent Surface

UbiPriSEQ—Deep Reinforcement Learning to Manage Privacy, Security, Energy, and QoS in 5G IoT HetNets

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