Ocean University of China

About: Ocean University of China is a education organization based out in Qingdao, China. It is known for research contribution in the topics: Population & Sea surface temperature. The organization has 27604 authors who have published 27886 publications receiving 440181 citations. The organization is also known as: Zhōngguó Hǎiyáng Dàxué & OUC.

Local Differential Privacy based Federated Learning for Internet of Things

Abstract: Internet of Vehicles (IoV) is a promising branch of the Internet of Things. IoV simulates a large variety of crowdsourcing applications such as Waze, Uber, and Amazon Mechanical Turk, etc. Users of these applications report the real-time traffic information to the cloud server which trains a machine learning model based on traffic information reported by users for intelligent traffic management. However, crowdsourcing application owners can easily infer users' location information, which raises severe location privacy concerns of the users. In addition, as the number of vehicles increases, the frequent communication between vehicles and the cloud server incurs unexpected amount of communication cost. To avoid the privacy threat and reduce the communication cost, in this paper, we propose to integrate federated learning and local differential privacy (LDP) to facilitate the crowdsourcing applications to achieve the machine learning model. Specifically, we propose four LDP mechanisms to perturb gradients generated by vehicles. The Three-Outputs mechanism is proposed which introduces three different output possibilities to deliver a high accuracy when the privacy budget is small. The output possibilities of Three-Outputs can be encoded with two bits to reduce the communication cost. Besides, to maximize the performance when the privacy budget is large, an optimal piecewise mechanism (PM-OPT) is proposed. We further propose a suboptimal mechanism (PM-SUB) with a simple formula and comparable utility to PM-OPT. Then, we build a novel hybrid mechanism by combining Three-Outputs and PM-SUB.

Deep water circulation in the Luzon Strait

Abstract: Deep water circulation in the Luzon Strait, which connects the Pacific deep circulation with the South China Sea throughflow, is investigated using a set of oceanographic observations combined with results from three numerical experiments. Both the in situ observations and the model show a deep water overflow in the Luzon Strait. Their results suggest that the deep Pacific water first flows into the Luzon Strait through the Bashi Channel (1.2 Sv, 1 Sv = 1 × 106 m3 s−1) and the Taltung Canyon (0.4 Sv), then turns southward along the Luzon Trough, and finally enters the South China Sea primarily through two gaps in the Heng-Chun Ridge. Overall, the mean transport of the Luzon Strait overflow is about 1.5 Sv. Results from numerical experiments suggest that strong diapycnal mixing in the South China Sea and Luzon Strait, which sustains the baroclinic pressure gradient across the Luzon Strait, is the primary driving mechanism of the deep circulation in the Luzon Strait.