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Jun Zhao

Researcher at Nanyang Technological University

Publications -  377
Citations -  8395

Jun Zhao is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Computer science & Differential privacy. The author has an hindex of 33, co-authored 333 publications receiving 4312 citations. Previous affiliations of Jun Zhao include Arizona State University & China Three Gorges University.

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Toward Secure Blockchain-Enabled Internet of Vehicles: Optimizing Consensus Management Using Reputation and Contract Theory

TL;DR: Li et al. as discussed by the authors proposed a two-stage soft security enhancement solution: miner selection and block verification, which evaluates candidates' reputation using both past interactions and recommended opinions from other vehicles The candidates with high reputation are selected to be active miners and standby miners in order to prevent internal collusion among active miners.
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Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices

TL;DR: A federated learning (FL) system leveraging a reputation mechanism to assist home appliance manufacturers to train a machine learning model based on customers’ data so that manufacturers can predict customers' requirements and consumption behaviors in the future.
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Artificial-Intelligence-Enabled Intelligent 6G Networks

TL;DR: In this paper, the authors proposed an Ai-enabled intelligent architecture for 6G networks to realize knowledge discovery, smart resource management, automatic network adjustment and intelligent service provisioning, where the architecture is divided into four layers: intelligent sensing layer, data mining and analytics layer, intelligent control layer and smart application layer.
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The use of injectable sonication-induced silk hydrogel for VEGF(165) and BMP-2 delivery for elevation of the maxillary sinus floor.

TL;DR: It is indicated that silk hydrogels can be used as an injectable vehicle to deliver multiple growth factors in a minimally invasive approach to regenerate irregular bony cavities.
Proceedings ArticleDOI

Collecting and Analyzing Multidimensional Data with Local Differential Privacy

TL;DR: Li et al. as discussed by the authors proposed novel LDP mechanisms for collecting a numeric attribute, whose accuracy is at least no worse (and usually better) than existing solutions in terms of worst-case noise variance.