Towards Communication-Efficient and Attack-Resistant Federated Edge Learning for Industrial Internet of Things

Federated Edge Learning (FEL) allows edge nodes to train a global deep learning model collaboratively for edge computing in the Industrial Internet of Things (IIoT), which significantly promotes the development of Industrial 4.0. However, FEL faces two critical challenges: communication overhead and data privacy. FEL suffers from expensive communication overhead when training large-scale multi-node models. Furthermore, due to the vulnerability of FEL to gradient leakage and label-flipping attacks, the training process of the global model is easily compromised by adversaries. To address these challenges, we propose a communication-efficient and privacy-enhanced asynchronous FEL framework for edge computing in IIoT. First, we introduce an asynchronous model update scheme to reduce the computation time that edge nodes wait for global model aggregation. Second, we propose an asynchronous local differential privacy mechanism, which improves communication efficiency and mitigates gradient leakage attacks by adding well-designed noise to the gradients of edge nodes. Third, we design a cloud-side malicious node detection mechanism to detect malicious nodes by testing the local model quality. Such a mechanism can avoid malicious nodes participating in training to mitigate label-flipping attacks. Extensive experimental studies on two real-world datasets demonstrate that the proposed framework can not only improve communication efficiency but also mitigate malicious attacks while its accuracy is comparable to traditional FEL frameworks.

An Unsupervised Aspect-Aware Recommendation Model with Explanation Text Generation

Review based recommendation utilizes both users’ rating records and the associated reviews for recommendation. Recently, with the rapid demand for explanations of recommendation results, reviews are used to train the encoder–decoder models for explanation text generation. As most of the reviews are general text without detailed evaluation, some researchers leveraged auxiliary information of users or items to enrich the generated explanation text. Nevertheless, the auxiliary data is not available in most scenarios and may suffer from data privacy problems. In this article, we argue that the reviews contain abundant semantic information to express the users’ feelings for various aspects of items, while these information are not fully explored in current explanation text generation task. To this end, we study how to generate more fine-grained explanation text in review based recommendation without any auxiliary data. Though the idea is simple, it is non-trivial since the aspect is hidden and unlabeled. Besides, it is also very challenging to inject aspect information for generating explanation text with noisy review input. To solve these challenges, we first leverage an advanced unsupervised neural aspect extraction model to learn the aspect-aware representation of each review sentence. Thus, users and items can be represented in the aspect space based on their historical associated reviews. After that, we detail how to better predict ratings and generate explanation text with the user and item representations in the aspect space. We further dynamically assign review sentences which contain larger proportion of aspect words with larger weights to control the text generation process, and jointly optimize rating prediction accuracy and explanation text generation quality with a multi-task learning framework. Finally, extensive experimental results on three real-world datasets demonstrate the superiority of our proposed model for both recommendation accuracy and explainability.

Assessing Brunei Darussalam Public and Private Sector Readiness Towards Big Data Application

This exploratory study aims to assess and investigate Brunei Darussalam’s readiness in developing and applying big data technologies for its public and private sectors, using Social, Technological, Environmental and Policy (STEP) framework. The results show that the population are digitally literate (Social) and utilises smart devices as well as internet network connectivity that is widely offered by the local telecommunications company (Technology). The government of Brunei Darussalam established multiple digital transformation initiatives including implementation of 5G connectivity as well as digital economy masterplan to digitally transformed in the near future (Environment). Regardless of the absence of national digital data privacy policy (Policy) in Brunei, the recent nation’s successful big data application in public sector – BruHealth Application – to contain Covid-19 community spread was achieved. Alas, the existence of such policy in the near future will create opportunities for the local private sectors to capitalise big data technologies to their business strategies.

Deep-Confidentiality : An IoT-Enabled Privacy-Preserving Framework for Unstructured Big Biomedical Data

Due to the Internet of Things evolution, the clinical data is exponentially growing and using smart technologies. The generated big biomedical data is confidential, as it contains a patient’s personal information and findings. Usually, big biomedical data is stored over the cloud, making it convenient to be accessed and shared. In this view, the data shared for research purposes helps to reveal useful and unexposed aspects. Unfortunately, sharing of such sensitive data also leads to certain privacy threats. Generally, the clinical data is available in textual format (e.g., perception reports). Under the domain of natural language processing, many research studies have been published to mitigate the privacy breaches in textual clinical data. However, there are still limitations and shortcomings in the current studies that are inevitable to be addressed. In this article, a novel framework for textual medical data privacy has been proposed as Deep-Confidentiality . The proposed framework improves Medical Entity Recognition (MER) using deep neural networks and sanitization compared to the current state-of-the-art techniques. Moreover, the new and generic utility metric is also proposed, which overcomes the shortcomings of the existing utility metric. It provides the true representation of sanitized documents as compared to the original documents. To check our proposed framework’s effectiveness, it is evaluated on the i2b2-2010 NLP challenge dataset, which is considered one of the complex medical data for MER. The proposed framework improves the MER with 7.8% recall, 7% precision, and 3.8% F1-score compared to the existing deep learning models. It also improved the data utility of sanitized documents up to 13.79%, where the value of the k is 3.

A Novel Hybrid Approach for Multi-Dimensional Data Anonymization for Apache Spark

Multi-dimensional data anonymization approaches (e.g., Mondrian) ensure more fine-grained data privacy by providing a different anonymization strategy applied for each attribute. Many variations of multi-dimensional anonymization have been implemented on different distributed processing platforms (e.g., MapReduce, Spark) to take advantage of their scalability and parallelism supports. According to our critical analysis on overheads, either existing iteration-based or recursion-based approaches do not provide effective mechanisms for creating the optimal number of and relative size of resilient distributed datasets (RDDs), thus heavily suffer from performance overheads. To solve this issue, we propose a novel hybrid approach for effectively implementing a multi-dimensional data anonymization strategy (e.g., Mondrian) that is scalable and provides high-performance. Our hybrid approach provides a mechanism to create far fewer RDDs and smaller size partitions attached to each RDD than existing approaches. This optimal RDD creation and operations approach is critical for many multi-dimensional data anonymization applications that create tremendous execution complexity. The new mechanism in our proposed hybrid approach can dramatically reduce the critical overheads involved in re-computation cost, shuffle operations, message exchange, and cache management.

Data Privacy Protection in News Crowdfunding in the Era of Artificial Intelligence

This paper aims to study the protection of data privacy in news crowdfunding in the era of artificial intelligence. This paper respectively quotes the encryption algorithm of artificial intelligence data protection and the BP neural network prediction model to analyze the data privacy protection in news crowdfunding in the artificial intelligence era. Finally, this paper also combines the questionnaire survey method to understand the public’s awareness of privacy. The results of this paper show that artificial intelligence can promote personal data awareness and privacy, improve personal data and privacy measures and methods, and improve the effectiveness and level of privacy and privacy. In the analysis, the survey found that male college students only have 81.1% of the cognition of personal trait information, only 78.5% of network trace information, and only 78.3% of female college students’ cognition of personal credit.

Assessing Brunei Darussalam Public and Private Sector Readiness Towards Big Data Application

This exploratory study aims to assess and investigate Brunei Darussalam’s readiness in developing and applying big data technologies for its public and private sectors, using Social, Technological, Environmental and Policy (STEP) framework. The results show that the population are digitally literate (Social) and utilises smart devices as well as internet network connectivity that is widely offered by the local telecommunications company (Technology). The government of Brunei Darussalam established multiple digital transformation initiatives including implementation of 5G connectivity as well as digital economy masterplan to digitally transformed in the near future (Environment). Regardless of the absence of national digital data privacy policy (Policy) in Brunei, the recent nation’s successful big data application in public sector – BruHealth Application – to contain Covid-19 community spread was achieved. Alas, the existence of such policy in the near future will create opportunities for the local private sectors to capitalise big data technologies to their business strategies.

A Survey on Privacy Preservation in Fog-Enabled Internet of Things

Despite the rapid growth and advancement in the Internet of Things (IoT ), there are critical challenges that need to be addressed before the full adoption of the IoT. Data privacy is one of the hurdles towards the adoption of IoT as there might be potential misuse of users’ data and their identity in IoT applications. Several researchers have proposed different approaches to reduce privacy risks. However, most of the existing solutions still suffer from various drawbacks, such as huge bandwidth utilization and network latency, heavyweight cryptosystems, and policies that are applied on sensor devices and in the cloud. To address these issues, fog computing has been introduced for IoT network edges providing low latency, computation, and storage services. In this survey, we comprehensively review and classify privacy requirements for an in-depth understanding of privacy implications in IoT applications. Based on the classification, we highlight ongoing research efforts and limitations of the existing privacy-preservation techniques and map the existing IoT schemes with Fog-enabled IoT schemes to elaborate on the benefits and improvements that Fog-enabled IoT can bring to preserve data privacy in IoT applications. Lastly, we enumerate key research challenges and point out future research directions.