In the current big data landscape, where data forms the cornerstone of myriad applications, it is crucial to establish the reliability and credibility of application outcomes through the prism of high-quality data. Nonetheless, data quality has been facing significant evaluation challenges due to the exponential increase in data volume and diversity. This paper introduces a novel big data quality scoring (BDQS) model, which is particularly designed for assessing the quality of large-scale datasets within the Hadoop MapReduce ecosystem. Unlike other models that either focus on smaller datasets or rely on sampling techniques, BDQS excels in providing comprehensive data quality assessment for substantial data sources. Specifically, BDQS identifies accuracy, completeness, consistency, timeliness, and correlation as critical dimensions of data quality, scores each dimension on a scale of 0 to 100, and derives an aggregate data quality score through binomial testing and standard normalization of these scores. This research advances a potent model for big data quality assessment and offers valuable insights for enhancing the reliability and applicability of large-scale datasets across various sectors.

Facial recognition (FR) systems are employed to identify and authenticate individuals. There has been a rise in privacy concerns regarding mass surveillance and unauthorized usages. As a result, one viable approach is adding adversarial noise to distort user profile images so that FR technology can be bypassed. Nonetheless, such approaches could be used by adversaries to avoid detection in surveillance footage and therefore evade identification. To combat this threat, a line of research efforts focuses on generative adversarial network (GAN)-based Denoising and Restoration to remove adversarial noise. In this paper, GAN-based methods are investigated experimentally for assessing their effectiveness. Particularly, three GAN-based approaches, i.e., Blind Face Restoration, Blur and Restore, and Image-to-image Translation, are extensively examined with several representative classification approaches. Our evaluation results show that GAN denoising schemes could improve image visual quality, but are ineffective to remove perturbations for privacy protection attached by Fawkes or Lowkey. We further discuss some future research directions on image transformation-based approaches, which can potentially improve the effectiveness.

The applications of machine learning (ML) in cyber-physical systems (CPS), such as the smart energy grid has increased significantly. While ML technology can be integrated into CPS, the security risk of ML technology has to be considered. In particular, adversarial examples provide inputs to a ML model with intentionally attached perturbations (noise) that could pose the model to make incorrect decisions. Perturbations are expected to be small or marginal so that adversarial examples could be invisible to humans, but can significantly affect the output of ML models. In this paper, we design a taxonomy to provide the problem space for investigating the adversarial example generation techniques based on state-of-the-art literature. We propose a three-dimensional framework containing three dimensions for adversarial attack scenarios (i.e., black-box, white-box, and gray-box), target type, and adversarial examples generation methods (gradient-based, score-based, decision-based, transfer- based, and others). Based on the designed taxonomy, we systematically review the existing research efforts on adversarial ML in representative CPS (i.e., transportation, healthcare, and energy). Furthermore, we provide one case study to demonstrate the impact of adversarial examples of attacks on a smart energy CPS deployment. The results indicate that the accuracy can decrease significantly from 92.62% to 55.42% with a 30% adversarial sample injection. Finally, we discuss potential countermeasures and future research directions for adversarial ML.

The next generation WiFi such as IEEE 802.11ad and 802.11ay can provide stringent Quality of Service (QoS) due to its support of contention free channel access called Service Period. IEEE 802.11ad supports two types of user traffic: isochronous and asynchronous. These user traffic need guaranteed Service Period duration before their periods. Hence, admission control plays an important role in an IEEE 802.11ad system. In an earlier work we studied admission control only for isochronous requests. In this paper, we present admission control and scheduling algorithms which can handle both types of requests. We devise a proportional fair and linear run time complexity algorithm that treats asynchronous requests as periodic requests, because of which it overallocates resources to the asynchronous requests. The conditions of possible performance loss due to this overallocation are analyzed. We provide detailed simulation results which show that presence of asynchronous request degrades performance of isochronous requests in terms of number of admitted requests and channel utilization. But, the smaller number of admitted isochronous requests perform better in terms of channel allocation time and delay.

While facial recognition (FR) has been widely used by businesses and governments for various purposes, it gives rise to privacy concerns once the consent of users is not handled properly. Hence, researchers have proposed methods to evade FR technology by attaching adversarial perturbations to user profile images. Nonetheless, image denoising-based methods have been proposed to increase the model robustness over adversarial examples. This paper investigates the impact of transformations on adversarial facial images. In particular, a simple but effective framework, TaD (Transformations as Denoising), is proposed to remove possible adversarial perturbations from user images generated by popular FR privacy protection frameworks. Extensive evaluations show the reliability of Fawkes and LowKey with various simple transformations. Experimental results indicate that simple transformations can impact the protection performance, and the choice of DNN-based facial feature extractors can enhance the robustness of facial images with adversarial perturbations. The experimental results also demonstrate strengths and weaknesses of FR methods and give suggestions for further improvements of privacy safeguard tools.

Mobile object tracking, which has broad applications, utilizes a large number of Internet of Things (IoT) devices to identify, record, and share the trajectory information of physical objects. Nonetheless, IoT devices are energy constrained and not feasible for deploying advanced tracking techniques due to significant computing requirements. To address these issues, in this paper, we develop an edge computing-based multivariate time series (EC-MTS) framework to accurately track mobile objects and exploit edge computing to offload its intensive computation tasks. Specifically, EC-MTS leverages statistical technique (i.e., vector auto regression (VAR)) to conduct arbitrary historical object trajectory data revisit and fit a best-effort trajectory model for accurate mobile object location prediction. Our framework offers the benefit of offloading computation intensive tasks from IoT devices by using edge computing infrastructure. We have validated the efficacy of EC-MTS and our experimental results demonstrate that EC-MTS framework could significantly improve mobile object tracking efficacy in terms of trajectory goodness-of-fit and location prediction accuracy of mobile objects. In addition, we extend our proposed EC-MTS framework to conduct multiple objects tracking in IoT systems.

The fast development of the Internet of Things (IoT) and deep learning enables learning useful patterns from the massive amount of collected data with sporadic nodes in IoT systems. Federated learning has received increasing attention in distributed machine learning where only intermediate parameters are exchanged with training samples that resided at local nodes. Nonetheless, most of the existing federated learning schemes assume a homogeneous distribution of data. The assumption, however, does not apply to IoT systems because of the heterogeneity of the IoT architecture. The nonindependent and identical distribution (non-IID) property in data volume and statistical distribution of IoT nodes can impact the performance of an aggregated global model that fits all nodes. Existing federated learning solutions for non-IID data sets either have to train additional models or require extra data exchange to check the node distribution. However, due to resource constraints in IoT systems, these approaches will increase the burden on limited computation capacity and cause network overhead. To address the issue, in this article, a novel weight-similarity-based client clustering (WSCC) approach is proposed, in which clients are split into different groups based on their data set distributions. An affinity-propagation-based method with the cosine distance of the client’s weight parameters is designed to iteratively and automatically determine dynamic clusters. The proposed approach is ideal for IoT systems since there are no auxiliary models and extra data transmissions are needed. Through the theoretical convergence analysis and empirical results, we show that our proposed WSCC scheme outperforms the representative federated learning schemes under different non-IID settings, achieving up to 20% improvements in accuracy.

The federated learning framework builds a deep learning model collaboratively by a group of connected devices via only sharing local parameter updates to the central parameter server. Nonetheless, the lack of transparency in the local data resource makes it prone to adversarial federated attacks, which have shown increasing ability to reduce learning performance. Existing research efforts either focus on the single-party attack with impractical perfect knowledge setting and limited stealthy ability or the random attack that has no control on attack effects. In this paper, we investigate a new multi-party adversarial attack with the imperfect knowledge of the target system. Controlled by an adversary, a number of compromised devices collaboratively launch targeted model poisoning attacks, intending to misclassify the targeted samples while maintaining stealthy under different detection strategies. Specifically, the compromised devices jointly minimize the loss function of model training in different scenarios. To overcome the update scaling problem, we develop a new boosting strategy by introducing two stealthy metrics. Via experimental results, we show that under both perfect knowledge and limited knowledge settings, the multi-party attack is capable of successfully evading detection strategies while guaranteeing the convergence. We also demonstrate that the learned model achieves the high accuracy on the targeted samples, which confirms the significant impact of the multi-party attack on federated learning systems.