Physical-Layer Assisted Privacy-Preserving Offloading in Mobile-Edge Computing

He, Xiaofan; Jin, Richeng; Dai, Huaiyu

doi:10.1109/icc.2019.8761166

Cited by 24 publications

(10 citation statements)

References 23 publications

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“…Similarly, He et al [30] developed a constrained Markov decision process based privacy-aware task offloading scheduling algorithm, which achieves the best possible delay and energy consumption with a high level of privacy. To solve the risk of disclosing possibly sensitive user data to eavesdroppers, He et al [31] studied a novel physical-layer assisted privacypreserving offloading scheme, in which the edge server proactively broadcasts jamming signals to impede eavesdropping attack and leverages full-duplex communication technique to effectively suppress the self-interference. In work [32], an energy-efficient computation offloading method with privacy preservation, was proposed to improve the load balance and energy consumption of all the edge nodes in edge computing-enabled 5G networks.…”

Section: Related Workmentioning

confidence: 99%

Privacy and Energy Co-Aware Data Aggregation Computation Offloading for Fog-Assisted IoT Networks

2020

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With the exponential growth of the data generated by Internet of Things (IoT) devices, computation offloading becomes a promising method to alleviate the computation burden of local IoT device and improve processing latency. In order to address the bottleneck problem of limited resources in IoT device more efficiently and provide security guarantee in data processing and forwarding process, in this paper, we propose a privacy and energy co-aware data aggregation computation offloading for fogassisted IoT networks. Specifically, a fog-assisted three-layer security computing architecture is developed to counteract security threats and enable the aggregation operation can be performed in ciphertext. Meanwhile, a momentum gradient descent based energy-efficient offloading decision algorithm is developed to minimize the total energy consumption of computation tasks, which can achieve the optimal value with fast convergence rate. Finally, the security and performance evaluations reveal that the developed data aggregation offloading scheme is a secure data processing scheme and achieves significant performance advantage in energy consumption. For example, the total energy consumption can be reduced by an average of 23.1% compared with benchmark PGCO solution.

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Section: Related Workmentioning

confidence: 99%

Privacy and Energy Co-Aware Data Aggregation Computation Offloading for Fog-Assisted IoT Networks

2020

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“…In [11], the physical layer security and non-orthogonal multiple access (NOMA) were adopted and the secure computation efficiency maximization problem was investigated. In [12], the MEC server proactively transmitted jamming signals to prevent information leakage, and the full-duplex communication technique was adopted to suppress the self-interference. In [13], physical layer security and NOMA were adopted to minimize the weighted sum-energy consumption and the secrecy outage probability of the MDs, respectively.…”

Section: A Relative Workmentioning

confidence: 99%

“…where B and p u are the channel bandwidth and transmit power of the MD, respectively. The power consumption of the MD is modeled as υf 3 (J/s), where υ is a constant factor determined by the switched capacitance of the MD [12]. Finally, after the computation at the SBS, the MD downloads the computation result at the last time interval with length τ 3 .…”

Section: B Computation Modelmentioning

confidence: 99%

Secure MISO Cognitive-Based Mobile Edge Computing With Wireless Power Transfer

2020

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The finite battery capacity, limited spectrum resource and the transmission security are three challenges in designing mobile edge computing (MEC) systems. In this paper, a framework for wireless powered secure multiple-input single-output (MISO) cognitive-based MEC-enabled networks is proposed, which integrates several technologies: physical-layer security, cooperative relaying, cognitive radio (CR), wireless power transfer (WPT) and MEC. Two optimization problems are formulated to maximize the number of computation bits (NCB) of the mobile device (MD) and minimize the total transmission power (TTP) of the primary transmitter (PT) and the SBS, respectively. The formulated problems are non-convex and hard to solve. Two two-phase methods with block coordinate decent (BCD) and Lagrangian dual decomposition methods are proposed to jointly design the beamforming vector of the PT, beamforming matrix of the secondary base station (SBS), the central processing unit (CPU) frequency, the transmit power, the number of the offloaded bits and the offloading time of the MD. Simulation results are presented to show the effectiveness of the proposed methods. INDEX TERMS Mobile edge computing, cognitive radio, physical-layer security, wireless power transfer, MISO.

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“…Hence, the MD may not be friendly, i.e., it may be a potential eavesdropper to eavesdrop the PT's transmission. To avoid the PT's information leakage, we adopt physical layer security technique, which exploits the characteristics of wireless channels to guarantee the secure transmission of users [19]- [21]. The secrecy rate for the PT is given as…”

Section: Problem Formulationmentioning

confidence: 99%

Robust Secure Wireless Powered MISO Cognitive Mobile Edge Computing

et al. 2020

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Wireless power transfer (WPT) and cognitive radio (CR) are two promising techniques in designing mobile-edge computing (MEC) systems. In this paper, we study a robust secure wireless powered multiple-input single-output (MISO) cognitive MEC system, which integrates several techniques: physical-layer security, WPT, CR, underlay spectrum sharing and MEC. Three optimization problems are formulated to minimize the total transmission power (TTP) of the primary transmitter (PT) and the secondary base station (SBS) under perfect channel state information (CSI) model, bounded CSI error model and the probabilistic CSI error model, respectively. The formulated problems are nonconvex and hard to solve. Three two-phase iterative optimization algorithms combined with Lagrangian dual, semidefinite relaxation (SDR), S-Procedure and Bernstein-type inequalities are proposed to jointly optimize the beamforming vectors of the PT and the SBS, the central processing unit (CPU) frequency and the transmit power of the MD. Simulation results are provided to verify the effectiveness of the proposed algorithms. INDEX TERMS Mobile edge computing, cognitive radio, underlay spectrum sharing, physical-layer security, wireless power transfer.

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Physical-Layer Assisted Privacy-Preserving Offloading in Mobile-Edge Computing

Cited by 24 publications

References 23 publications

Privacy and Energy Co-Aware Data Aggregation Computation Offloading for Fog-Assisted IoT Networks

Privacy and Energy Co-Aware Data Aggregation Computation Offloading for Fog-Assisted IoT Networks

Secure MISO Cognitive-Based Mobile Edge Computing With Wireless Power Transfer

Robust Secure Wireless Powered MISO Cognitive Mobile Edge Computing

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