Federated learning (FL) is a promising new technology in the field of IoT intelligence. However, exchanging model-related data in FL may leak the sensitive information of participants. To address this problem, we propose a novel privacy-preserving FL framework based on an innovative chained secure multiparty computing technique, named chain-PPFL. Our scheme mainly leverages two mechanisms: 1) single-masking mechanism that protects information exchanged between participants and 2) chained-communication mechanism that enables masked information to be transferred between participants with a serial chain frame. We conduct extensive simulation-based experiments using two public data sets (MNIST and CIFAR-100) by comparing both training accuracy and leak defence with other state-of-the-art schemes. We set two data sample distributions (IID and NonIID) and three training models (CNN, MLP, and L-BFGS) in our experiments. The experimental results demonstrate that the chain-PPFL scheme can achieve practical privacy preservation (equivalent to differential privacy with $\epsilon $ approaching zero) for FL with some cost of communication and without impairing the accuracy and convergence speed of the training model.

Privacy-Preserving Federated Learning Framework Based on Chained Secure Multiparty Computing

The development of smart cities and deep learning technology is changing our physical world to a cyber world. As one of the main applications, the Internet of Vehicles has been developing rapidly. However, privacy leakage and delay problem for data collection remain as the key concerns behind the fast development of the cyber intelligence technologies. If the original data collected are directly uploaded to the cloud for processing, it will bring huge load pressure and delay to the network communication. Moreover, during this process, it will lead to the leakage of data privacy. To this end, in this article we design a data collection and preprocessing scheme based on deep learning, which adopts the semisupervised learning algorithm of data augmentation and label guessing. Data filtering is performed at the edge layer, and a large amount of similar data and irrelevant data are cleared. If the edge device cannot process some complex data independently, it will send the processed and reliable data to the cloud for further processing, which maximizes the protection of user privacy. Our method significantly reduces the amount of data uploaded to the cloud, and meanwhile protects the user's data privacy effectively.

Privacy-Enhanced Data Collection Based on Deep Learning for Internet of Vehicles

Under the combination of Internet-of-Things (IoT) technology and traditional industry, the Industrial IoT (IIoT) came into being and received wide attention from all walks of life. With the increase of the number of IIOT devices in industrial environments, security threats, and quality of service (QoS) issues increase drastically. Internal attack is one type of important security threat that makes service environment worse and less reliable. However, there is no unified and fine-grained trust evaluation mechanism to deal with the threats of internal attack and improve QoS of IIoT. To this end, a unified trustworthy environment based on edge computing is established and maintained, which can timely detect malicious service providers and service consumers, filter unreal information, and recommend credible service providers. Edge computing is introduced as an effective service access point, since it supports collecting service records to perform trust evaluations. Moreover, a service selection method is designed to choose the corresponding trustworthy and reliable service providers based on the trust evaluation and the recording criterion, which has distinctive advantages in the succinct trust management, convenient searching service, and accurate service matching. Experiments validated the feasibility of the proposed trustworthy environment.

A Unified Trustworthy Environment Establishment Based on Edge Computing in Industrial IoT

Privacy-preserving quality prediction for edge-based IoT services

Edge-based auditing method for data security in resource-constrained Internet of Things

Edge-based differential privacy computing for sensor–cloud systems

An IA is an abnormal swelling of cerebral vessels, and a subset of these IAs can rupture causing aneurysmal subarachnoid hemorrhage (aSAH), often resulting in death or severe disability. Few studies have used an appropriate method of feature selection combined with machine learning by analyzing transcriptomic sequencing data to identify new molecular biomarkers. Following gene ontology (GO) and enrichment analysis, we found that the distinct status of IAs could lead to differential innate immune responses using all 913 differentially expressed genes, and considering that there are numerous irrelevant and redundant genes, we propose a mixed filter- and wrapper-based feature selection. First, we used the Fast Correlation-Based Filter (FCBF) algorithm to filter a large number of irrelevant and redundant genes in the raw dataset, and then used the wrapper feature selection method based on the he Multi-layer Perceptron (MLP) neural network and the Particle Swarm Optimization (PSO), accuracy (ACC) and mean square error (MSE) were then used as the evaluation criteria. Finally, we constructed a novel 10-gene signature (YIPF1, RAB32, WDR62, ANPEP, LRRCC1, AADAC, GZMK, WBP2NL, PBX1, and TOR1B) by the proposed two-stage hybrid algorithm FCBF-MLP-PSO and used different machine learning models to predict the rupture status in IAs. The highest ACC value increased from 0.817 to 0.919 (12.5% increase), the highest area under ROC curve (AUC) value increased from 0.87 to 0.94 (8.0% increase), and all evaluation metrics improved by approximately 10% after being processed by our proposed gene selection algorithm. Therefore, these 10 informative genes used to predict rupture status of IAs can be used as complements to imaging examinations in the clinic, meanwhile, this selected gene signature also provides new targets and approaches for the treatment of ruptured IAs.

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A two-stage hybrid gene selection algorithm combined with machine learning models to predict the rupture status in intracranial aneurysms

A fully analog pipelined deep neural network (DNN) accelerator is proposed, which is constructed by using pipeline registers based on master-slave switched capacitors. The idea of the master-slave switched capacitors is an analog equivalent of the delayed flip-flop (D-FF) which has been used as a digital pipeline register. To estimate the performance of the pipeline register, it is applied to a conventional DNN which performs non-pipeline operation. Compared with the conventional DNN, the cycle time is reduced by 61.5% and data rate is increased by 160%. The accuracy reaches 99.6% in MNIST classification test. The energy consumption per classification is reduced by 88.2% to 0.128μJ, achieving an energy efficiency of 1.05TOPS⁄W and a throughput of 0.538 TOPS in 180nm technology node. 

A Fully Analog Deep Neural Network Inference Accelerator with Pipeline Registers Based on Master-Slave Switched Capacitors

: With rapid development of economy and technology, people’s life style has changed significantly. Specifically, physical activity has been greatly reduced while the consumption of fast food and processed food has increased, and then the obesity has become more and more common and serious problem. In order to solve this problem, a heat monitoring and management system based on mobile terminals is proposed and realized. By embedding the heat monitoring and management module into mobile terminals, the proposed system allows users to facilitate their weight management regardless of time and space.

Yaxin Mei

Papers

Edge-based differential privacy computing for sensor–cloud systems

A two-stage hybrid gene selection algorithm combined with machine learning models to predict the rupture status in intracranial aneurysms

A Fully Analog Deep Neural Network Inference Accelerator with Pipeline Registers Based on Master-Slave Switched Capacitors

-83-Design and Research of Mobile Terminal-Based Heat Monitoring and Management System