Secure Authentication-Management human-centric Scheme for trusting personal resource information on mobile cloud computing with blockchain

Kim, Hyun Woo; Jeong, Young‐Sik

doi:10.1186/s13673-018-0136-7

Cited by 71 publications

(38 citation statements)

References 26 publications

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“…Second, the blockchain server records all the activities in the system such as information about requesting and logging devices and mining blocks. Note that the blockchain server in the BlockDeepNet system uses a lightweight consensus mechanism such as PBFT [13], which does not require proof-of-work task and supports less resource-intensive mining tasks [14]. 5) Iteration and epoch: In the BlockDeepNet system, iterations refer to the multiple steps of DL tasks performed by an IoT device to obtain an effective local model.…”

Section: )mentioning

confidence: 99%

BlockDeepNet: A Blockchain-Based Secure Deep Learning for IoT Network

2019

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The recent development in IoT and 5G translates into a significant growth of Big data in 5G—envisioned industrial automation. To support big data analysis, Deep Learning (DL) has been considered the most promising approach in recent years. Note, however, that designing an effective DL paradigm for IoT has certain challenges such as single point of failure, privacy leak of IoT devices, lack of valuable data for DL, and data poisoning attacks. To this end, we present BlockDeepNet, a Blockchain-based secure DL that combines DL and blockchain to support secure collaborative DL in IoT. In BlockDeepNet, collaborative DL is performed at the device level to overcome privacy leak and obtain enough data for DL, whereas blockchain is employed to ensure the confidentiality and integrity of collaborative DL in IoT. The experimental evaluation shows that BlockDeepNet can achieve higher accuracy for DL with acceptable latency and computational overhead of blockchain operation.

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Section: )mentioning

confidence: 99%

BlockDeepNet: A Blockchain-Based Secure Deep Learning for IoT Network

2019

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“…Lin et al introduces a cross-platform IPC mechanism called XBinder [21] to enable remote processes among multi-user communication for mobile applications to cooperate with local or remote services without forming a complicate network. However, despite the remarkable improvements with respect to performance [22], these design choices only target mobile devices connected in the same network, and thus they will not work when devices are moving to the other networks. To address this issue, we adopted a group-to-group network architecture to help flexibly switch the roles of devices during run-time and allow reconfiguring the network in multiple topologies.…”

Section: Related Workmentioning

confidence: 99%

Enabling multi-hop remote method invocation in device-to-device networks

Le¹,

Clyde²,

Kwon³

2019

Hum. Cent. Comput. Inf. Sci.

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IntroductionResource sharing or computation offloading on mobile networks can bring a lot of benefits, the approaches in this domain if possible, can be widely applied to IoT networks or ubiquitous cities. In this section, we will go through the several limitations of current technologies, as well as our motivation to build a middleware that overcomes these obstacles by extending Remote Method Invocation method and multi-hop capability to enable resource sharing over mobile networks.Low-end devices always have trouble running intensive resource-consuming applications such as image or video processing, which remarkably slow down its speed and drain energy. One well-known solution is having the device participate in a collaboration in which it can offload or migrate intensive code portions [1-3] onto another device or cloud server with copious resource capacity, have them execute the code and wait for responses [4,5]. Although the idea is straightforward, code offloading has not been widely applied in the mobile industry due to several issues: it requires AbstractTo avoid shrinking down the performance and preserve energy, low-end mobile devices can collaborate with the nearby ones by offloading computation intensive code. However, despite the long research history, code offloading is dilatory and unfit for applications that require rapidly consecutive requests per short period. Even though Remote Procedure Call (RPC) is apparently one possible approach that can address this problem, the RPC-based or message queue-based techniques are obsolete or unwieldy for mobile platforms. Moreover, the need of accessibility beyond the limit reach of the device-to-device (D2D) networks originates another problem. This article introduces a new software framework to overcome these shortcomings by enabling routing RPC architecture on multiple group device-to-device networks. Our framework provides annotations for declaring distribution decision and out-of-box components that enable peer-to-peer offloading, even when a client app and the service provider do not have a direct network link or Internet connectivity. This article also discusses the two typical mobile applications that built on top of the framework for chatting and remote browsing services, as well as the empirical experiments with actual test-bed devices to unveil the low overhead conduct and similar performance as RPC in reality. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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“…The proposed crowd-intelligence platform is decentralized using blockchain technology and is not hosted on a centralized RC. The ESs are owned by many individuals and behave as blockchain masternodes (MNs) [18]. Without the agreement of more than a half MNs, the smart contract cannot be changed arbitrarily [19].…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, the raw data transferring between publisher and workers can proceed with the nearest ES/MNs serving as intermediary nodes, which can avoid excess bandwidth and delayed response. The ES/MNs charge a small fees from the publisher and workers to keep the security state of the blockchain, and earn money from it [18], [19]. With the help of blockchain smart contract, the involved ESs can maximize profits with strong Nash equilibrium [20] and the proposed ecosystem can attract more ESs to serve as blockchain masternodes.…”

Section: Introductionmentioning

confidence: 99%

A Blockchain-Enabled Trustless Crowd-Intelligence Ecosystem on Mobile Edge Computing

Wang

Bhargava

et al. 2019

IEEE Trans. Ind. Inf.

105

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Crowd-intelligence tries to gather, process, infer and ascertain massive useful information by utilizing the intelligence of crowds or distributed computers, which has great potential in Industrial Internet of Things (IIoT). A crowd-intelligence ecosystem involves three stakeholders, namely the platform, workers (e.g., individuals, sensors or processors), and task publisher. The stakeholders have no mutual trust but interest conflict, which means bad cooperation of them. Due to lack of trust, transferring raw data (e.g., pictures or video clips) between publisher and workers requires the remote platform center to serve as a relay node, which implies network congestion. First we use a rewardpenalty model to align the incentives of stakeholders. Then the predefined rules are implemented using blockchain smart contract on many edge servers of the mobile edge computing network, which together function as a trustless hybrid humanmachine crowd-intelligence platform. As edge servers are near to workers and publisher, network congestion can be effectively improved. Further, we proved the existence of the only one strong Nash equilibrium, which can maximize the interests of involved edge servers and make the ecosystem bigger. Theoretical analysis and experiments validate the proposed method respectively.Index Terms-Mobile edge computing, blockchain smart contract, crowd-intelligence ecosystem, trustless, hybrid humanmachine, reward and penalty, strong Nash equilibrium.

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Secure Authentication-Management human-centric Scheme for trusting personal resource information on mobile cloud computing with blockchain

Cited by 71 publications

References 26 publications

BlockDeepNet: A Blockchain-Based Secure Deep Learning for IoT Network

BlockDeepNet: A Blockchain-Based Secure Deep Learning for IoT Network

Enabling multi-hop remote method invocation in device-to-device networks

A Blockchain-Enabled Trustless Crowd-Intelligence Ecosystem on Mobile Edge Computing

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