Blockchain technology has gained considerable attention, with an escalating interest in a plethora of numerous applications, ranging from data management, financial services, cyber security, IoT, and food science to healthcare industry and brain research. There has been a remarkable interest witnessed in utilizing applications of blockchain for the delivery of safe and secure healthcare data management. Also, blockchain is reforming the traditional healthcare practices to a more reliable means, in terms of effective diagnosis and treatment through safe and secure data sharing. In the future, blockchain could be a technology that may potentially help in personalized, authentic, and secure healthcare by merging the entire real-time clinical data of a patient’s health and presenting it in an up-to-date secure healthcare setup. In this paper, we review both the existing and latest developments in the field of healthcare by implementing blockchain as a model. We also discuss the applications of blockchain, along with the challenges faced and future perspectives.
Blockchain decentralization not only ensures transparency of transactions to eliminate need of trusting third party, but also makes the transactions of the network to be publicly accessible to all the participating peers in the network. As a result, data anonymity and confidentiality are compromised making several business enterprises and industrialists hesitant to adopt the technology. Although research community has proposed various privacy-preserving solutions for blockchain, however, they still lack in efficiency resulting in distrust of industries in opting for the technology. This study is conducted for contributing to the existing body of knowledge corresponding to privacy in blockchains. The fundamental goal of this study is to delve into privacy vulnerabilities of the blockchain network in a permissionless setting by identifying non-trivial roots of factors causing privacy breach in blockchain and presenting limitation of existing privacy preserving mechanisms. Studies with superficial comparison of privacy preserving techniques are available in literature but a detailed and in-depth analysis of their limitations and causes of privacy breach in blockchain is yet not done. Therefore, in this paper we first present comprehensive analysis of various privacy breaching factors of the blockchain networks. Next, we discuss existing cryptographic and noncryptographic solutions in literature. We found out that these existing privacy preserving mechanisms have their own set of limitations and hence are inefficient at current point of time. The existing privacy preserving mechanisms need further consideration of the research community before they're widely adopted and benchmarked. Therefore, in the end, we identified some future directions that need to be addressed to model an efficient privacy preserving mechanism for wider adoption of the blockchain technology.
Halal food is gaining attention among Muslims and non-Muslims alike due to its nature of ensuring food is free from any impurities or contamination and hygienically prepared. The growing demand for Halal food has resulted in several food-producing companies to certify their products as Halal. However, with existing supply chains, there is no authenticity of these products being Halal. To ensure Halal food authenticity, the technology of blockchain is proposed as a viable solution. In this chapter, the applicability and usability of blockchain technology in food supply chain management systems is studied and highlighted. The study depicts that how trackability and traceability of the blockchain networks can effectively aid in maintaining the Halal integrity of food products by presenting various use cases. Technological shift for food supply chains over blockchains will result in more transparent, secure, and resilient supply chains. This will bring variety of health and economic benefits to food producing business and consumers.
Image classification of a visual scene based on visibility is significant due to the rise in readily available automated solutions. Currently, there are only two known spectrums of image visibility i.e., dark, and bright. However, normal environments include semi-dark scenarios. Hence, visual extremes that will lead to the accurate extraction of image features should be duly discarded. Fundamentally speaking there are two broad methods to perform visual scene-based image classification, i.e., machine learning (ML) methods and computer vision methods. In ML, the issues of insufficient data, sophisticated hardware and inadequate image classifier training time remain significant problems to be handled. These techniques fail to classify the visual scene-based images with high accuracy. The other alternative is computer vision (CV) methods, which also have major issues. CV methods do provide some basic procedures which may assist in such classification but, to the best of our knowledge, no CV algorithm exists to perform such classification, i.e., these do not account for semi-dark images in the first place. Moreover, these methods do not provide a well-defined protocol to calculate images’ content visibility and thereby classify images. One of the key algorithms for calculation of images’ content visibility is backed by the HSL (hue, saturation, lightness) color model. The HSL color model allows the visibility calculation of a scene by calculating the lightness/luminance of a single pixel. Recognizing the high potential of the HSL color model, we propose a novel framework relying on the simple approach of the statistical manipulation of an entire image’s pixel intensities, represented by HSL color model. The proposed algorithm, namely, Relative Perceived Luminance Classification (RPLC) uses the HSL (hue, saturation, lightness) color model to correctly identify the luminosity values of the entire image. Our findings prove that the proposed method yields high classification accuracy (over 78%) with a small error rate. We show that the computational complexity of RPLC is much less than that of the state-of-the-art ML algorithms.
With the widespread of blockchain technology, preserving the anonymity and confidentiality of transactions have become crucial. An enormous portion of blockchain research is dedicated to the design and development of privacy protocols but not much has been achieved for proper assessment of these solutions. To mitigate the gap, we have first comprehensively classified the existing solutions based on blockchain fundamental building blocks (i.e., smart contracts, cryptography, and hashing). Next, we investigated the evaluation criteria used for validating these techniques. The findings depict that the majority of privacy solutions are validated based on computing resources i.e., memory, time, storage, throughput, etc., only, which is not sufficient. Hence, we have additionally identified and presented various other factors that strengthen or weaken blockchain privacy. Based on those factors, we have formulated an evaluation framework to analyze the efficiency of blockchain privacy solutions. Further, we have introduced a concept of privacy precision that is a quantifiable measure to empirically assess privacy efficiency in blockchains. The calculation of privacy precision will be based on the effectiveness and strength of various privacy protecting attributes of a solution and the associated risks. Finally, we conclude the paper with some open research challenges and future directions. Our study can serve as a benchmark for empirical assessment of blockchain privacy.
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