Mubashir Husain Rehmani

Muhammad Muneeb Saad, Mubashir Husain Rehmani, Ruairi O'Reilly

Imbalanced image datasets are commonly available in the domain of biomedical image analysis. Biomedical images contain diversified features that are significant in predicting targeted diseases. Generative Adversarial Networks (GANs) are utilized to address the data limitation problem via the generation of synthetic images. Training challenges such as mode collapse, non-convergence, and instability degrade a GAN's performance in synthesizing diversified and high-quality images. In this work, MSG-SAGAN, an attention-guided multi-scale gradient GAN architecture is proposed to model the relationship between long-range dependencies of biomedical image features and improves the training performance using a flow of multi-scale gradients at multiple resolutions in the layers of generator and discriminator models. The intent is to reduce the impact of mode collapse and stabilize the training of GAN using an attention mechanism with multi-scale gradient learning for diversified X-ray image synthesis. Multi-scale Structural Similarity Index Measure (MS-SSIM) and Frechet Inception Distance (FID) are used to identify the occurrence of mode collapse and evaluate the diversity of synthetic images generated. The proposed architecture is compared with the multi-scale gradient GAN (MSG-GAN) to assess the diversity of generated synthetic images. Results indicate that the MSG-SAGAN outperforms MSG-GAN in synthesizing diversified images as evidenced by the MS-SSIM and FID scores.

Cristina-Madalina Dragan, Muhammad Muneeb Saad, Mubashir Husain Rehmani et al.

Publicly available diabetic retinopathy (DR) datasets are imbalanced, containing limited numbers of images with DR. This imbalance contributes to overfitting when training machine learning classifiers. The impact of this imbalance is exacerbated as the severity of the DR stage increases, affecting the classifiers' diagnostic capacity. The imbalance can be addressed using Generative Adversarial Networks (GANs) to augment the datasets with synthetic images. Generating synthetic images is advantageous if high-quality and diversified images are produced. To evaluate the quality and diversity of synthetic images, several evaluation metrics, such as Multi-Scale Structural Similarity Index (MS-SSIM), Cosine Distance (CD), and Fréchet Inception Distance (FID) are used. Understanding the effectiveness of each metric in evaluating the quality and diversity of GAN-based synthetic images is critical to select images for augmentation. To date, there has been limited analysis of the appropriateness of these metrics in the context of biomedical imagery. This work contributes an empirical assessment of these evaluation metrics as applied to synthetic Proliferative DR imagery generated by a Deep Convolutional GAN (DCGAN). Furthermore, the metrics' capacity to indicate the quality and diversity of synthetic images and a correlation with classifier performance is undertaken. This enables a quantitative selection of synthetic imagery and an informed augmentation strategy. Results indicate that FID is suitable for evaluating the quality, while MS-SSIM and CD are suitable for evaluating the diversity of synthetic imagery. Furthermore, the superior performance of Convolutional Neural Network (CNN) and EfficientNet classifiers, as indicated by the F1 and AUC scores, for the augmented datasets demonstrates the efficacy of synthetic imagery to augment the imbalanced dataset.

Muhammad Muneeb Saad, Mubashir Husain Rehmani, Ruairi O'Reilly

Biomedical image datasets can be imbalanced due to the rarity of targeted diseases. Generative Adversarial Networks play a key role in addressing this imbalance by enabling the generation of synthetic images to augment datasets. It is important to generate synthetic images that incorporate a diverse range of features to accurately represent the distribution of features present in the training imagery. Furthermore, the absence of diverse features in synthetic images can degrade the performance of machine learning classifiers. The mode collapse problem impacts Generative Adversarial Networks' capacity to generate diversified images. Mode collapse comes in two varieties: intra-class and inter-class. In this paper, both varieties of the mode collapse problem are investigated, and their subsequent impact on the diversity of synthetic X-ray images is evaluated. This work contributes an empirical demonstration of the benefits of integrating the adaptive input-image normalization with the Deep Convolutional GAN and Auxiliary Classifier GAN to alleviate the mode collapse problems. Synthetically generated images are utilized for data augmentation and training a Vision Transformer model. The classification performance of the model is evaluated using accuracy, recall, and precision scores. Results demonstrate that the DCGAN and the ACGAN with adaptive input-image normalization outperform the DCGAN and ACGAN with un-normalized X-ray images as evidenced by the superior diversity scores and classification scores.

Muhammad Muneeb Saad, Mubashir Husain Rehmani, Ruairi O'Reilly

In biomedical image analysis, data imbalance is common across several imaging modalities. Data augmentation is one of the key solutions in addressing this limitation. Generative Adversarial Networks (GANs) are increasingly being relied upon for data augmentation tasks. Biomedical image features are sensitive to evaluating the efficacy of synthetic images. These features can have a significant impact on metric scores when evaluating synthetic images across different biomedical imaging modalities. Synthetically generated images can be evaluated by comparing the diversity and quality of real images. Multi-scale Structural Similarity Index Measure and Cosine Distance are used to evaluate intra-class diversity, while Frechet Inception Distance is used to evaluate the quality of synthetic images. Assessing these metrics for biomedical and non-biomedical imaging is important to investigate an informed strategy in evaluating the diversity and quality of synthetic images. In this work, an empirical assessment of these metrics is conducted for the Deep Convolutional GAN in a biomedical and non-biomedical setting. The diversity and quality of synthetic images are evaluated using different sample sizes. This research intends to investigate the variance in diversity and quality across biomedical and non-biomedical imaging modalities. Results demonstrate that the metrics scores for diversity and quality vary significantly across biomedical-to-biomedical and biomedical-to-non-biomedical imaging modalities.

Muhammad Muneeb Saad, Mubashir Husain Rehmani, Ruairi O'Reilly

Biomedical image datasets can be imbalanced due to the rarity of targeted diseases. Generative Adversarial Networks play a key role in addressing this imbalance by enabling the generation of synthetic images to augment datasets. It is important to generate synthetic images that incorporate a diverse range of features to accurately represent the distribution of features present in the training imagery. Furthermore, the absence of diverse features in synthetic images can degrade the performance of machine learning classifiers. The mode collapse problem can impact a Generative Adversarial Network's capacity to generate diversified images. Mode collapse comes in two varieties: intra-class and inter-class. In this paper, the intra-class mode collapse problem is investigated, and its subsequent impact on the diversity of synthetic X-ray images is evaluated. This work contributes an empirical demonstration of the benefits of integrating the adaptive input-image normalization for the Deep Convolutional GAN to alleviate the intra-class mode collapse problem. Results demonstrate that the DCGAN with adaptive input-image normalization outperforms DCGAN with un-normalized X-ray images as evident by the superior diversity scores.

Muhammad Muneeb Saad, Ruairi O'Reilly, Mubashir Husain Rehmani

In biomedical image analysis, the applicability of deep learning methods is directly impacted by the quantity of image data available. This is due to deep learning models requiring large image datasets to provide high-level performance. Generative Adversarial Networks (GANs) have been widely utilized to address data limitations through the generation of synthetic biomedical images. GANs consist of two models. The generator, a model that learns how to produce synthetic images based on the feedback it receives. The discriminator, a model that classifies an image as synthetic or real and provides feedback to the generator. Throughout the training process, a GAN can experience several technical challenges that impede the generation of suitable synthetic imagery. First, the mode collapse problem whereby the generator either produces an identical image or produces a uniform image from distinct input features. Second, the non-convergence problem whereby the gradient descent optimizer fails to reach a Nash equilibrium. Thirdly, the vanishing gradient problem whereby unstable training behavior occurs due to the discriminator achieving optimal classification performance resulting in no meaningful feedback being provided to the generator. These problems result in the production of synthetic imagery that is blurry, unrealistic, and less diverse. To date, there has been no survey article outlining the impact of these technical challenges in the context of the biomedical imagery domain. This work presents a review and taxonomy based on solutions to the training problems of GANs in the biomedical imaging domain. This survey highlights important challenges and outlines future research directions about the training of GANs in the domain of biomedical imagery.

Muneeb Ul Hassan, Mubashir Husain Rehmani, Jinjun Chen

Over the past decade, blockchain technology has attracted a huge attention from both industry and academia because it can be integrated with a large number of everyday applications of modern information and communication technologies (ICT). Peer-to-peer (P2P) architecture of blockchain enhances these applications by providing strong security and trust-oriented guarantees, such as immutability, verifiability, and decentralization. Despite these incredible features that blockchain technology brings to these ICT applications, recent research has indicated that the strong guarantees are not sufficient enough and blockchain networks may still be prone to various security, privacy, and reliability issues. In order to overcome these issues, it is important to identify the anomalous behaviour within the actionable time frame. In this article, we provide an in-depth survey regarding integration of anomaly detection models in blockchain technology. For this, we first discuss how anomaly detection can aid in ensuring security of blockchain based applications. Then, we demonstrate certain fundamental evaluation metrics and key requirements that can play a critical role while developing anomaly detection models for blockchain. Afterwards, we present a thorough survey of various anomaly detection models from the perspective of each layer of blockchain. Finally, we conclude the article by highlighting certain important challenges alongside discussing how they can serve as future research directions for new researchers in the field.

Muneeb Ul Hassan, Mubashir Husain Rehmani, Maaz Rehan et al.

Background/Introduction: Integrating cognitive radio (CR) with traditional wireless networks is helping solve the problem of spectrum scarcity in an efficient manner. The opportunistic and dynamic spectrum access features of CR provide the functionality to its unlicensed users to utilize the underutilized spectrum at the time of need because CR nodes can sense vacant bands of spectrum and can also access them to carry out communication. Various capabilities of CR nodes depend upon efficient and continuous reporting of data with each other and centralized base stations, which in turn can cause leakage in privacy. Experimental studies have shown that the privacy of CR users can be compromised easily during the cognition cycle, because they are knowingly or unknowingly sharing various personally identifiable information (PII), such as location, device ID, signal status, etc. In order to preserve this privacy leakage, various privacy preserving strategies have been developed by researchers, and according to us differential privacy is the most significant among them.

Khadija Hafeez, Donna OShea, Thomas Newe et al.

High frequency reporting of energy consumption data in smart grids can be used to infer sensitive information regarding the consumer's life style and poses serious security and privacy threats. Differential privacy (DP) based privacy models for smart grids ensure privacy when analysing energy consumption data for billing and load monitoring. However, DP models for smart grids are vulnerable to collusion attack where an adversary colludes with malicious smart meters and un-trusted aggregator in order to get private information from other smart meters. We first show the vulnerability of DP based privacy model for smart grids against collusion attacks to establish the need of a collusion resistant model privacy model. Then, we propose an Enhanced Differential Private Noise Cancellation Model for Load Monitoring and Billing for Smart Meters (E-DPNCT) which not only provides resistance against collusion attacks but also protects the privacy of the smart grid data while providing accurate billing and load monitoring. We use differential privacy with a split noise cancellation protocol with multiple master smart meters (MSMs) to achieve colluison resistance. We did extensive comparison of our E-DPNCT model with state of the art attack resistant privacy preserving models such as EPIC for collusion attack. We simulate our E-DPNCT model with real time data which shows significant improvement in privacy attack scenarios. Further, we analyze the impact of selecting different sensitivity parameters for calibrating DP noise over the privacy of customer electricity profile and accuracy of electricity data aggregation such as load monitoring and billing.

Muhammad Islam, Mubashir Husain Rehmani, Jinjun Chen

Permissioned blockchain such as Hyperledger fabric enables a secure supply chain model in Industrial Internet of Things (IIoT) through multichannel and private data collection mechanisms. Sharing of Industrial data including private data exchange at every stage between supply chain partners helps to improve product quality, enable future forecast, and enhance management activities. However, the existing data sharing and querying mechanism in Hyperledger fabric is not suitable for supply chain environment in IIoT because the queries are evaluated on actual data stored on ledger which consists of sensitive information such as business secrets, and special discounts offered to retailers and individuals. To solve this problem, we propose a differential privacy-based permissioned blockchain using Hyperledger fabric to enable private data sharing in supply chain in IIoT (DH-IIoT). We integrate differential privacy into the chaindcode (smart contract) of Hyperledger fabric to achieve privacy preservation. As a result, the query response consists of perturbed data which protects the sensitive information in the ledger. The proposed work (DH-IIoT) is evaluated by simulating a permissioned blockchain using Hyperledger fabric. We compare our differential privacy integrated chaincode of Hyperledger fabric with the default chaincode setting of Hyperledger fabric for supply chain scenario. The results confirm that the proposed work maintains 96.15% of accuracy in the shared data while guarantees the protection of sensitive ledger's data.

Khadija Hafeez, Mubashir Husain Rehmani, Donna OShea

Highly accurate profiles of consumers daily energy usage are reported to power grid via smart meters which enables smart grid to effectively regulate power demand and supply. However, consumers energy consumption pattern can reveal personal and sensitive information regarding their lifestyle. Therefore, to ensure users privacy, differentially distributed noise is added to the original data. This technique comes with a trade off between privacy of the consumer versus utility of the data in terms of providing services like billing, Demand Response schemes, and Load Monitoring. In this paper, we propose a technique - Differential Privacy with Noise Cancellation Technique (DPNCT) - to maximize utility in aggregated load monitoring and fair billing while preserving users privacy by using noise cancellation mechanism on differentially private data. We introduce noise to the sensitive data stream before it leaves smart meters in order to guarantee privacy at individual level. Further, we evaluate the effects of different periodic noise cancelling schemes on privacy and utility i.e., billing and load monitoring. Our proposed scheme outperforms the existing scheme in terms of preserving the privacy while accurately calculating the bill.

Muneeb Ul Hassan, Mubashir Husain Rehmani, Jinjun Chen

Traditional smart grid energy auctions cannot directly be integrated in blockchain due to its decentralized nature. Therefore, research works are being carried out to propose efficient decentralized auctions for energy trading. Since, blockchain is a novel paradigm which ensures trust, but it also comes up with a curse of high computation and communication complexity which eventually causes resource scarcity. Therefore, there is a need to develop and encourage development of greener and computational-friendly auctions to carry out decentralized energy trading. In this paper, we first provide a thorough motivation of decentralized auctions over traditional auctions. Afterwards, we provide in-depth design requirements that can be taken into consideration while developing such auctions. After that, we analyze technical works that have developed blockchain based energy auctions from green perspective. Finally, we summarize the article by providing challenges and possible future research directions of blockchain based energy auction from green viewpoint.

Muneeb Ul Hassan, Mubashir Husain Rehmani, Jinjun Chen

The desire to overcome reliability issues of distributed energy resources (DERs) lead researchers to development of a novel concept named as virtual power plant (VPP). VPPs are supposed to carry out intelligent, secure, and smart energy trading among prosumers, buyers, and generating stations along with providing efficient energy management. Therefore, integrating blockchain in a decentralized VPP network emerged as a new paradigm, and recent experiments over this integration have shown fruitful results. However, this decentralization also suffers with energy management, trust, reliability, and efficiency issues due to the dynamic nature of DERs. In order to overcome this, in this paper, we first work over providing an efficient energy management strategy for VPP to enhance demand response, then we propose an energy oriented trading and block mining protocol and name it as proof of energy market (PoEM). To enhance it further, we integrate differential privacy in PoEM and propose a Private PoEM (PPoEM) model. Collectively, we propose a private decentralized VPP trading model and named it as Virtual Private Trading (VPT) model. We further carry out extensive theoretical analysis and derive step-by-step valuations for market race probability, market stability probability, energy trading expectation, winning state probability, and prospective leading time profit values. Afterwards, we carry out simulation-based experiments of our proposed model. The performance evaluation and theoretical analysis of our VPT model make it one of the most viable models for blockchain based VPP networks as compared to other state-of-the-art works.

Muneeb Ul Hassan, Mubashir Husain Rehmani, Jia Tina Du et al.

In order to efficiently provide demand side management (DSM) in smart grid, carrying out pricing on the basis of real-time energy usage is considered to be the most vital tool because it is directly linked with the finances associated with smart meters. Hence, every smart meter user wants to pay the minimum possible amount along with getting maximum benefits. In this context, usage based dynamic pricing strategies of DSM plays their role and provide users with specific incentives that help shaping their load curve according to the forecasted load. However, these reported real-time values can leak privacy of smart meter users, which can lead to serious consequences such as spying, etc. Moreover, most dynamic pricing algorithms charge all users equally irrespective of their contribution in causing peak factor. Therefore, in this paper, we propose a modified usage based dynamic pricing mechanism that only charges the users responsible for causing peak factor. We further integrate the concept of differential privacy to protect the privacy of real-time smart metering data. To calculate accurate billing, we also propose a noise adjustment method. Finally, we propose Demand Response enhancing Differential Pricing (DRDP) strategy that effectively enhances demand response along with providing dynamic pricing to smart meter users. We also carry out theoretical analysis for differential privacy guarantees and for cooperative state probability to analyze behavior of cooperative smart meters. The performance evaluation of DRDP strategy at various privacy parameters show that the proposed strategy outperforms previous mechanisms in terms of dynamic pricing and privacy preservation.

Muneeb Ul Hassan, Mubashir Husain Rehmani, Jinjun Chen

The concept of differential privacy emerged as a strong notion to protect database privacy in an untrusted environment. Later on, researchers proposed several variants of differential privacy in order to preserve privacy in certain other scenarios, such as real-time cyber physical systems. Since then, differential privacy has rigorously been applied to certain other domains which has the need of privacy preservation. One such domain is decentralized blockchain based smart metering, in which smart meters acting as blockchain nodes sent their real-time data to grid utility databases for real-time reporting. This data is further used to carry out statistical tasks, such as load forecasting, demand response calculation, etc. However, in case if any intruder gets access to this data it can leak privacy of smart meter users. In this context, differential privacy can be used to protect privacy of this data. In this chapter, we carry out comparison of four variants of differential privacy (Laplace, Gaussian, Uniform, and Geometric) in blockchain based smart metering scenario. We test these variants on smart metering data and carry out their performance evaluation by varying different parameters. Experimental outcomes shows at low privacy budget ($\varepsilon$) and at low reading sensitivity value ($δ$), these privacy preserving mechanisms provide high privacy by adding large amount of noise. However, among these four privacy preserving parameters Geometric parameters is more suitable for protecting high peak values and Laplace mechanism is more suitable for protecting low peak values at ($\varepsilon$ = 0.01).

Muneeb Ul Hassan, Mubashir Husain Rehmani, Jinjun Chen

Blockchain has received a widespread attention because of its decentralized, tamper-proof, and transparent nature. Blockchain works over the principle of distributed, secured, and shared ledger, which is used to record, and track data within a decentralized network. This technology has successfully replaced certain systems of economic transactions in organizations and has the potential to overtake various industrial business models in future. Blockchain works over peer-to-peer (P2P) phenomenon for its operation and does not require any trusted-third party authorization for data tracking and storage. The information stored in blockchain is distributed throughout the decentralized network and is usually protected using cryptographic hash functions. Since the beginning of blockchain technology, its use in different applications is increasing exponentially, but this increased use has also raised some questions regarding privacy and security of data being stored in it. Protecting privacy of blockchain data using data perturbation strategy such as differential privacy could be a novel approach to overcome privacy issues in blockchain. In this article, we cover the topic of integration of differential privacy in each layer of blockchain and in certain blockchain based scenarios. Moreover, we highlight some future challenges and application scenarios in which integration of differential privacy in blockchain can produce fruitful results.

Vishal Sharma, Ilsun You, Karl Andersson et al.

With an enormous range of applications, Internet of Things (IoT) has magnetized industries and academicians from everywhere. IoT facilitates operations through ubiquitous connectivity by providing Internet access to all the devices with computing capabilities. With the evolution of wireless infrastructure, the focus from simple IoT has been shifted to smart, connected and mobile IoT (M-IoT) devices and platforms, which can enable low-complexity, low-cost and efficient computing through sensors, machines, and even crowdsourcing. All these devices can be grouped under a common term of M-IoT. Even though the positive impact on applications has been tremendous, security, privacy and trust are still the major concerns for such networks and an insufficient enforcement of these requirements introduces non-negligible threats to M-IoT devices and platforms. Thus, it is important to understand the range of solutions which are available for providing a secure, privacy-compliant, and trustworthy mechanism for M-IoT. There is no direct survey available, which focuses on security, privacy, trust, secure protocols, physical layer security and handover protections in M-IoT. This paper covers such requisites and presents comparisons of state-the-art solutions for IoT which are applicable to security, privacy, and trust in smart and connected M-IoT networks. Apart from these, various challenges, applications, advantages, technologies, standards, open issues, and roadmap for security, privacy and trust are also discussed in this paper.

Muneeb Ul Hassan, Mubashir Husain Rehmani, Jinjun Chen

Modern cyber physical systems (CPSs) has widely being used in our daily lives because of development of information and communication technologies (ICT).With the provision of CPSs, the security and privacy threats associated to these systems are also increasing. Passive attacks are being used by intruders to get access to private information of CPSs. In order to make CPSs data more secure, certain privacy preservation strategies such as encryption, and k-anonymity have been presented in the past. However, with the advances in CPSs architecture, these techniques also needs certain modifications. Meanwhile, differential privacy emerged as an efficient technique to protect CPSs data privacy. In this paper, we present a comprehensive survey of differential privacy techniques for CPSs. In particular, we survey the application and implementation of differential privacy in four major applications of CPSs named as energy systems, transportation systems, healthcare and medical systems, and industrial Internet of things (IIoT). Furthermore, we present open issues, challenges, and future research direction for differential privacy techniques for CPSs. This survey can serve as basis for the development of modern differential privacy techniques to address various problems and data privacy scenarios of CPSs.