On Lightweight Privacy-Preserving Collaborative Learning for IoT Objects

The Internet of Things (IoT) will be a main data generation infrastructure for achieving better system intelligence. This paper considers the design and implementation of a practical privacy-preserving collaborative learning scheme, in which a curious learning coordinator trains a better machine learning model based on the data samples contributed by a number of IoT objects, while the confidentiality of the raw forms of the training data is protected against the coordinator. Existing distributed machine learning and data encryption approaches incur significant computation and communication overhead, rendering them ill-suited for resource-constrained IoT objects. We study an approach that applies independent Gaussian random projection at each IoT object to obfuscate data and trains a deep neural network at the coordinator based on the projected data from the IoT objects. This approach introduces light computation overhead to the IoT objects and moves most workload to the coordinator that can have sufficient computing resources. Although the independent projections performed by the IoT objects address the potential collusion between the curious coordinator and some compromised IoT objects, they significantly increase the complexity of the projected data. In this paper, we leverage the superior learning capability of deep learning in capturing sophisticated patterns to maintain good learning performance. Extensive comparative evaluation shows that this approach outperforms other lightweight approaches that apply additive noisification for differential privacy and/or support vector machines for learning in the applications with light data pattern complexities.Comment: 12 pages,IOTDI 201

Physical Layer Security for Visible Light Communication Systems:A Survey

Due to the dramatic increase in high data rate services and in order to meet the demands of the fifth-generation (5G) networks, researchers from both academia and industry are exploring advanced transmission techniques, new network architectures and new frequency spectrum such as the visible light spectra. Visible light communication (VLC) particularly is an emerging technology that has been introduced as a promising solution for 5G and beyond. Although VLC systems are more immune against interference and less susceptible to security vulnerabilities since light does not penetrate through walls, security issues arise naturally in VLC channels due to their open and broadcasting nature, compared to fiber-optic systems. In addition, since VLC is considered to be an enabling technology for 5G, and security is one of the 5G fundamental requirements, security issues should be carefully addressed and resolved in the VLC context. On the other hand, due to the success of physical layer security (PLS) in improving the security of radio-frequency (RF) wireless networks, extending such PLS techniques to VLC systems has been of great interest. Only two survey papers on security in VLC have been published in the literature. However, a comparative and unified survey on PLS for VLC from information theoretic and signal processing point of views is still missing. This paper covers almost all aspects of PLS for VLC, including different channel models, input distributions, network configurations, precoding/signaling strategies, and secrecy capacity and information rates. Furthermore, we propose a number of timely and open research directions for PLS-VLC systems, including the application of measurement-based indoor and outdoor channel models, incorporating user mobility and device orientation into the channel model, and combining VLC and RF systems to realize the potential of such technologies

Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation

In this paper we evaluate several well-known and widely used machine learning algorithms for regression in the energy disaggregation task. Specifically, the Non-Intrusive Load Monitoring approach was considered and the K-Nearest-Neighbours, Support Vector Machines, Deep Neural Networks and Random Forest algorithms were evaluated across five datasets using seven different sets of statistical and electrical features. The experimental results demonstrated the importance of selecting both appropriate features and regression algorithms. Analysis on device level showed that linear devices can be disaggregated using statistical features, while for non-linear devices the use of electrical features significantly improves the disaggregation accuracy, as non-linear appliances have non-sinusoidal current draw and thus cannot be well parametrized only by their active power consumption. The best performance in terms of energy disaggregation accuracy was achieved by the Random Forest regression algorithm.Peer reviewedFinal Published versio

A Lightweight Deep Learning Model for The Early Detection of Epilepsy

Epilepsy is a neurological disorder and non communicable disease which affects patient's health, During this seizure occurrence normal brain function activity will be interrupted. It may happen anywhere and anytime so it leads to very dangerous problems like sudden unexpected death. Worldwide seizure affected people are around 65% million. So it must be considered as serious problem for the early prediction.  A number of different types of screening tests will be conducted to assess the severity of the symptoms such as EEG,MRI, ECG, and ECG. There are several reasons why EEG signals are used, including their affordability, portability, and ability to display. The proposed model used bench-marked CHB-MIT EEG datasets for the implementation of early prediction of epilepsy ensures its seriousness and leads to perfect diagnosis. Researchers proposed Various ML /DL methods to  try for the early prediction of epilepsy but still it has some challenges in terms of efficiency and precision Seizure detection techniques typically employ the use of convolutional neural networks (CNN) and a bidirectional short- and long-term memory (Bi-LSTM) model in the realm of deep learning. This method leverages the strengths of both models to effectively analyze electroencephalogram (EEG) data and detect seizure patterns. These light weight models have been found to be effective in automatically detecting seizures in deep learning techniques with an accuracy rate of up to 96.87%. Hence, this system has the potential to be utilized for categorizing other types of physiological signals too, but additional research is required to confirm this

Light-weighted CNN-Attention based architecture for Hand Gesture Recognition via ElectroMyography

Advancements in Biological Signal Processing (BSP) and Machine-Learning (ML) models have paved the path for development of novel immersive Human-Machine Interfaces (HMI). In this context, there has been a surge of significant interest in Hand Gesture Recognition (HGR) utilizing Surface-Electromyogram (sEMG) signals. This is due to its unique potential for decoding wearable data to interpret human intent for immersion in Mixed Reality (MR) environments. To achieve the highest possible accuracy, complicated and heavy-weighted Deep Neural Networks (DNNs) are typically developed, which restricts their practical application in low-power and resource-constrained wearable systems. In this work, we propose a light-weighted hybrid architecture (HDCAM) based on Convolutional Neural Network (CNN) and attention mechanism to effectively extract local and global representations of the input. The proposed HDCAM model with 58,441 parameters reached a new state-of-the-art (SOTA) performance with 82.91% and 81.28% accuracy on window sizes of 300 ms and 200 ms for classifying 17 hand gestures. The number of parameters to train the proposed HDCAM architecture is 18.87 times less than its previous SOTA counterpart

Cyber-Physical Attacks Targeting Communication-Assisted Protection Schemes

The dependence of modern societies on electric energy is ever increasing by the emergence of smart cities and electric vehicles. This is while unprecedented number of cyber-physical hazards are threatening the integrity and availability of the power grid on a daily basis. On one hand, physical integrity of power systems is under threat by more frequent natural disasters and intentional attacks. On the other hand, the cyber vulnerability of power grids is on the rise by the emergence of smart grid technologies. This underlines an imminent need for the modeling and examination of power grid vulnerabilities to cyber-physical attacks. This paper examines the vulnerability of the communication-assisted protection schemes like permissive overreaching transfer trip to cyberattacks using a co-simulation platform. The simulation results show that the transient angle stability of power systems can be jeopardized by cyberattacks on the communication-assisted protection schemes. To address this vulnerability, two physical solutions including the deployment of communication channel redundancy, and a more advanced communicated-assisted protection scheme, i.e., directional comparison unblocking scheme (DCUB), are considered and tested. The proposed solutions address the vulnerability of the communication-assisted protection schemes to distributed denial of service attack to some extent. Yet, the simulation results show the vulnerability of the proposed solutions to sophisticated cyberattacks like false data injection attacks. This highlights the need for the development of cyber-based solutions for communication channel monitoring