255 research outputs found
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
A Survey on Blockchain & Cloud Integration
Blockchain is one of the emerging technologies with the potential to disrupt
many application domains. Cloud is an on-demand service paradigm facilitating
the availability of shared resources for data storage and computation. In
recent years, the integration of blockchain and cloud has received significant
attention for ensuring efficiency, transparency, security and even for offering
better cloud services in the form of novel service models. In order to exploit
the full potential of blockchain-cloud integration, it is essential to have a
clear understanding on the existing works within this domain. To facilitate
this, there have been several survey papers, however, none of them covers the
aspect of blockchain-cloud integration from a service-oriented perspective.
This paper aims to fulfil this gap by providing a service oriented review of
blockchain-cloud integration. Indeed, in this survey, we explore different
service models into which blockchain has been integrated. For each service
model, we review the existing works and present a comparative analysis so as to
offer a clear and concise view in each category.Comment: Accepted for publication in the 23rd International Conference on
Computer and Information Technology (ICCIT), 202
Performance Evaluation of Differential Privacy Mechanisms in Blockchain based Smart Metering
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 () and at low reading
sensitivity value (), 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 ( = 0.01).Comment: Submitte
A Survey on Blockchain for Big Data: Approaches, Opportunities, and Future Directions
Big data has generated strong interest in various scientific and engineering
domains over the last few years. Despite many advantages and applications,
there are many challenges in big data to be tackled for better quality of
service, e.g., big data analytics, big data management, and big data privacy
and security. Blockchain with its decentralization and security nature has the
great potential to improve big data services and applications. In this article,
we provide a comprehensive survey on blockchain for big data, focusing on
up-to-date approaches, opportunities, and future directions. First, we present
a brief overview of blockchain and big data as well as the motivation behind
their integration. Next, we survey various blockchain services for big data,
including blockchain for secure big data acquisition, data storage, data
analytics, and data privacy preservation. Then, we review the state-of-the-art
studies on the use of blockchain for big data applications in different
vertical domains such as smart city, smart healthcare, smart transportation,
and smart grid. For a better understanding, some representative blockchain-big
data projects are also presented and analyzed. Finally, challenges and future
directions are discussed to further drive research in this promising area
A Blockchain Framework for Managing and Monitoring Data in Multi-Site Clinical Trials
The cost of conducting multi-site clinical trials has significantly increased
over time, with site monitoring, data management, and amendments being key
drivers. Clinical trial data management approaches typically rely on a central
database, and require manual efforts to encode and maintain data capture and
reporting requirements. To reduce the administrative burden, time, and effort
of ensuring data integrity and privacy in multi-site trials, we propose a novel
data management framework based on permissioned blockchain technology. We
demonstrate how our framework, which uses smart contracts and private channels,
enables confidential data communication, protocol enforcement, and and an
automated audit trail. We compare this framework with the traditional data
management approach and evaluate its effectiveness in satisfying the major
requirements of multi-site clinical trials. We show that our framework ensures
enforcement of IRB-related regulatory requirements across multiple sites and
stakeholders
SoK: Blockchain Solutions for Forensics
As the digitization of information-intensive processes gains momentum in
nowadays, the concern is growing about how to deal with the ever-growing
problem of cybercrime. To this end, law enforcement officials and security
firms use sophisticated digital forensics techniques for analyzing and
investigating cybercrimes. However, multi-jurisdictional mandates,
interoperability issues, the massive amount of evidence gathered (multimedia,
text etc.) and multiple stakeholders involved (law enforcement agencies,
security firms etc.) are just a few among the various challenges that hinder
the adoption and implementation of sound digital forensics schemes. Blockchain
technology has been recently proposed as a viable solution for developing
robust digital forensics mechanisms. In this paper, we provide an overview and
classification of the available blockchain-based digital forensic tools, and we
further describe their main features. We also offer a thorough analysis of the
various benefits and challenges of the symbiotic relationship between
blockchain technology and the current digital forensics approaches, as proposed
in the available literature. Based on the findings, we identify various
research gaps, and we suggest future research directions that are expected to
be of significant value both for academics and practitioners in the field of
digital forensics
Spitz: A Verifiable Database System
Databases in the past have helped businesses maintain and extract insights
from their data. Today, it is common for a business to involve multiple
independent, distrustful parties. This trend towards decentralization
introduces a new and important requirement to databases: the integrity of the
data, the history, and the execution must be protected. In other words, there
is a need for a new class of database systems whose integrity can be verified
(or verifiable databases).
In this paper, we identify the requirements and the design challenges of
verifiable databases.We observe that the main challenges come from the need to
balance data immutability, tamper evidence, and performance. We first consider
approaches that extend existing OLTP and OLAP systems with support for
verification. We next examine a clean-slate approach, by describing a new
system, Spitz, specifically designed for efficiently supporting immutable and
tamper-evident transaction management. We conduct a preliminary performance
study of both approaches against a baseline system, and provide insights on
their performance
Towards Trusted and Intelligent Cyber-Physical Systems: A Security-by-Design Approach
The complexity of cyberattacks in Cyber-Physical Systems (CPSs) calls for a
mechanism that can evaluate the operational behaviour and security without
negatively affecting the operation of live systems. In this regard, Digital
Twins (DTs) are revolutionizing the CPSs. DTs strengthen the security of CPSs
throughout the product lifecycle, while assuming that the DT data is trusted,
providing agility to predict and respond to real-time changes. However,
existing DTs solutions in CPS are constrained with untrustworthy data
dissemination among multiple stakeholders and timely course correction. Such
limitations reinforce the significance of designing trustworthy distributed
solutions with the ability to create actionable insights in real-time. To do
so, we propose a framework that focuses on trusted and intelligent DT by
integrating blockchain and Artificial Intelligence (AI). Following a hybrid
approach, the proposed framework not only acquires process knowledge from the
specifications of the CPS, but also relies on AI to learn security threats
based on sensor data. Furthermore, we integrate blockchain to safeguard product
lifecycle data. We discuss the applicability of the proposed framework for the
automotive industry as a CPS use case. Finally, we identify the open challenges
that impede the implementation of intelligence-driven architectures in CPSs.Comment: 9 pages, 4 figure
Achieving cybersecurity in blockchain-based systems: a survey
With The Increase In Connectivity, The Popularization Of Cloud Services, And The Rise Of The Internet Of Things (Iot), Decentralized Approaches For Trust Management Are Gaining Momentum. Since Blockchain Technologies Provide A Distributed Ledger, They Are Receiving Massive Attention From The Research Community In Different Application Fields. However, This Technology Does Not Provide With Cybersecurity By Itself. Thus, This Survey Aims To Provide With A Comprehensive Review Of Techniques And Elements That Have Been Proposed To Achieve Cybersecurity In Blockchain-Based Systems. The Analysis Is Intended To Target Area Researchers, Cybersecurity Specialists And Blockchain Developers. For This Purpose, We Analyze 272 Papers From 2013 To 2020 And 128 Industrial Applications. We Summarize The Lessons Learned And Identify Several Matters To Foster Further Research In This AreaThis work has been partially funded by MINECO, Spain grantsTIN2016-79095-C2-2-R (SMOG-DEV) and PID2019-111429RB-C21 (ODIO-COW); by CAM, Spain grants S2013/ICE-3095 (CIBERDINE),P2018/TCS-4566 (CYNAMON), co-funded by European Structural Funds (ESF and FEDER); by UC3M-CAM grant CAVTIONS-CM-UC3M; by the Excellence Program for University Researchers, Spain; and by Consejo Superior de Investigaciones CientĂficas (CSIC), Spain under the project LINKA20216 (“Advancing in cybersecurity technologies”, i-LINK+ program)
Deep Learning meets Blockchain for Automated and Secure Access Control
Access control is a critical component of computer security, governing access
to system resources. However, designing policies and roles in traditional
access control can be challenging and difficult to maintain in dynamic and
complex systems, which is particularly problematic for organizations with
numerous resources. Furthermore, traditional methods suffer from issues such as
third-party involvement, inefficiency, and privacy gaps, making transparent and
dynamic access control an ongoing research problem. Moreover detecting
malicious activities and identifying users who are not behaving appropriately
can present notable difficulties. To address these challenges, we propose
DLACB, a Deep Learning Based Access Control Using Blockchain, as a solution to
decentralized access control. DLACB uses blockchain to provide transparency,
traceability, and reliability in various domains such as medicine, finance, and
government while taking advantage of deep learning to not rely on predefined
policies and eventually automate access control. With the integration of
blockchain and deep learning for access control, DLACB can provide a general
framework applicable to various domains, enabling transparent and reliable
logging of all transactions. As all data is recorded on the blockchain, we have
the capability to identify malicious activities. We store a list of malicious
activities in the storage system and employ a verification algorithm to
cross-reference it with the blockchain. We conduct measurements and comparisons
of the smart contract processing time for the deployed access control system in
contrast to traditional access control methods, determining the time overhead
involved. The processing time of DLBAC demonstrates remarkable stability when
exposed to increased request volumes.Comment: arXiv admin note: text overlap with arXiv:2303.1475
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