An efficient linkable group signature for payer tracing in anonymous cryptocurrencies

Cryptocurrencies, led by bitcoin launched in 2009, have obtained wide attention in recent years. Anonymous cryptocurrencies are highly essential since users need to preserve their privacy when conducting transactions. However, some users might misbehave with the cover of anonymity such as rampant trafficking or extortion. Thus, it is important to balance anonymity and accountability of anonymous cryptocurrencies. In this paper, we address this issue by proposing a linkable group signature for signing cryptocurrency transactions, which can be used to trace a payer\u27s identity in consortium blockchain based anonymous cryptocurrencies, in case a payer misbehaves in the system. The anonymity can be retained if a user behaves honestly. We prove that the proposed scheme achieves full-anonymity, full-traceability and linkability in the random oracle model. Implementation of the proposed linkable group signature scheme demonstrates its high efficiency and thus, can be adopted in anonymous cryptocurrencies in reality

An efficient linkable group signature for payer tracing in anonymous cryptocurrencies

Cryptocurrencies, led by bitcoin launched in 2009, have obtained wide attention in recent years. Anonymous cryptocurrencies are highly essential since users need to preserve their privacy when conducting transactions. However, some users might misbehave with the cover of anonymity such as rampant trafficking or extortion. Thus, it is important to balance anonymity and accountability of anonymous cryptocurrencies. In this paper, we address this issue by proposing a linkable group signature for signing cryptocurrency transactions, which can be used to trace a payer\u27s identity in consortium blockchain based anonymous cryptocurrencies, in case a payer misbehaves in the system. The anonymity can be retained if a user behaves honestly. We prove that the proposed scheme achieves full-anonymity, full-traceability and linkability in the random oracle model. Implementation of the proposed linkable group signature scheme demonstrates its high efficiency and thus, can be adopted in anonymous cryptocurrencies in reality

Towards Secure and Intelligent Diagnosis: Deep Learning and Blockchain Technology for Computer-Aided Diagnosis Systems

Cancer is the second leading cause of death across the world after cardiovascular disease. The survival rate of patients with cancerous tissue can significantly decrease due to late-stage diagnosis. Nowadays, advancements of whole slide imaging scanners have resulted in a dramatic increase of patient data in the domain of digital pathology. Large-scale histopathology images need to be analyzed promptly for early cancer detection which is critical for improving patient's survival rate and treatment planning. Advances of medical image processing and deep learning methods have facilitated the extraction and analysis of high-level features from histopathological data that could assist in life-critical diagnosis and reduce the considerable healthcare cost associated with cancer. In clinical trials, due to the complexity and large variance of collected image data, developing computer-aided diagnosis systems to support quantitative medical image analysis is an area of active research. The first goal of this research is to automate the classification and segmentation process of cancerous regions in histopathology images of different cancer tissues by developing models using deep learning-based architectures. In this research, a framework with different modules is proposed, including (1) data pre-processing, (2) data augmentation, (3) feature extraction, and (4) deep learning architectures. Four validation studies were designed to conduct this research. (1) differentiating benign and malignant lesions in breast cancer (2) differentiating between immature leukemic blasts and normal cells in leukemia cancer (3) differentiating benign and malignant regions in lung cancer, and (4) differentiating benign and malignant regions in colorectal cancer. Training machine learning models, disease diagnosis, and treatment often requires collecting patients' medical data. Privacy and trusted authenticity concerns make data owners reluctant to share their personal and medical data. Motivated by the advantages of Blockchain technology in healthcare data sharing frameworks, the focus of the second part of this research is to integrate Blockchain technology in computer-aided diagnosis systems to address the problems of managing access control, authentication, provenance, and confidentiality of sensitive medical data. To do so, a hierarchical identity and attribute-based access control mechanism using smart contract and Ethereum Blockchain is proposed to securely process healthcare data without revealing sensitive information to an unauthorized party leveraging the trustworthiness of transactions in a collaborative healthcare environment. The proposed access control mechanism provides a solution to the challenges associated with centralized access control systems and ensures data transparency and traceability for secure data sharing, and data ownership