A review of the state of the art in privacy and security in the eHealth cloud

The proliferation and usefulness of cloud computing in eHealth demands high levels of security and privacy for health records. However, eHealth clouds pose serious security and privacy concerns for sensitive health data. Therefore, practical and effective methods for security and privacy management are essential to preserve the privacy and security of the data. To review the current research directions in security and privacy in eHealth clouds, this study has analysed and summarized the state of the art technologies and approaches reported in security and privacy in the eHealth cloud. An extensive review covering 132 studies from several peer-reviewed databases such as IEEE Xplore was conducted. The relevant studies were reviewed and summarized in terms of their benefits and risks. This study also compares several research works in the domain of data security requirements. This paper will provide eHealth stakeholders and researchers with extensive knowledge and information on current research trends in the areas of privacy and security

New Conditional Privacy-preserving Encryption Schemes in Communication Network

Nowadays the communication networks have acted as nearly the most important fundamental infrastructure in our human society. The basic service provided by the communication networks are like that provided by the ubiquitous public utilities. For example, the cable television network provides the distribution of information to its subscribers, which is much like the water or gas supply systems which distribute the commodities to citizens. The communication network also facilitates the development of many network-based applications such as industrial pipeline controlling in the industrial network, voice over long-term evolution (VoLTE) in the mobile network and mixture reality (MR) in the computer network, etc. Since the communication network plays such a vital role in almost every aspect of our life, undoubtedly, the information transmitted over it should be guarded properly. Roughly, such information can be categorized into either the communicated message or the sensitive information related to the users. Since we already got cryptographical tools, such as encryption schemes, to ensure the confidentiality of communicated messages, it is the sensitive personal information which should be paid special attentions to. Moreover, for the benefit of reducing the network burden in some instances, it may require that only communication information among legitimated users, such as streaming media service subscribers, can be stored and then relayed in the network. In this case, the network should be empowered with the capability to verify whether the transmitted message is exchanged between legitimated users without leaking the privacy of those users. Meanwhile, the intended receiver of a transmitted message should be able to identify the exact message sender for future communication. In order to cater to those requirements, we re-define a notion named conditional user privacy preservation. In this thesis, we investigate the problem how to preserve user conditional privacy in pubic key encryption schemes, which are used to secure the transmitted information in the communication networks. In fact, even the term conditional privacy preservation has appeared in existing works before, there still have great differences between our conditional privacy preservation definition and the one proposed before. For example, in our definition, we do not need a trusted third party (TTP) to help tracing the sender of a message. Besides, the verification of a given encrypted message can be done without any secret. In this thesis, we also introduce more desirable features to our redefined notion user conditional privacy preservation. In our second work, we consider not only the conditional privacy of the message sender but also that of the intended message receiver. This work presents a new encryption scheme which can be implemented in communication networks where there exists a blacklist containing a list of blocked communication channels, and each of them is established by a pair of sender and receiver. With this encryption scheme, a verifier can confirm whether one ciphertext is belonging to a legitimated communication channel without knowing the exact sender and receiver of that ciphertext. With our two previous works, for a given ciphertext, we ensure that no one except its intended receiver can identify the sender. However, the receiver of one message may behave dishonest when it tries to retrieve the real message sender, which incurs the problem that the receiver of a message might manipulate the origin of the message successfully for its own benefit. To tackle this problem, we present a novel encryption scheme in our third work. Apart from preserving user conditional privacy, this work also enforces the receiver to give a publicly verifiable proof so as to convince others that it is honest during the process of identifying the actual message sender. In our forth work, we show our special interest in the access control encryption, or ACE for short, and find this primitive can inherently achieve user conditional privacy preservation to some extent. we present a newly constructed ACE scheme in this work, and our scheme has advantages over existing ACE schemes in two aspects. Firstly, our ACE scheme is more reliable than existing ones since we utilize a distributed sanitizing algorithm and thus avoid the so called single point failure happened in ACE systems with only one sanitizer. Then, since the ciphertext and key size of our scheme is more compact than that of the existing ACE schemes, our scheme enjoys better scalability

Securing clouds using cryptography and traffic classification

Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction. Over the last decade, cloud computing has gained popularity and wide acceptance, especially within the health sector where it offers several advantages such as low costs, flexible processes, and access from anywhere. Although cloud computing is widely used in the health sector, numerous issues remain unresolved. Several studies have attempted to review the state of the art in eHealth cloud privacy and security however, some of these studies are outdated or do not cover certain vital features of cloud security and privacy such as access control, revocation and data recovery plans. This study targets some of these problems and proposes protocols, algorithms and approaches to enhance the security and privacy of cloud computing with particular reference to eHealth clouds. Chapter 2 presents an overview and evaluation of the state of the art in eHealth security and privacy. Chapter 3 introduces different research methods and describes the research design methodology and processes used to carry out the research objectives. Of particular importance are authenticated key exchange and block cipher modes. In Chapter 4, a three-party password-based authenticated key exchange (TPAKE) protocol is presented and its security analysed. The proposed TPAKE protocol shares no plaintext data; all data shared between the parties are either hashed or encrypted. Using the random oracle model (ROM), the security of the proposed TPAKE protocol is formally proven based on the computational Diffie-Hellman (CDH) assumption. Furthermore, the analysis included in this chapter shows that the proposed protocol can ensure perfect forward secrecy and resist many kinds of common attacks such as man-in-the-middle attacks, online and offline dictionary attacks, replay attacks and known key attacks. Chapter 5 proposes a parallel block cipher (PBC) mode in which blocks of cipher are processed in parallel. The results of speed performance tests for this PBC mode in various settings are presented and compared with the standard CBC mode. Compared to the CBC mode, the PBC mode is shown to give execution time savings of 60%. Furthermore, in addition to encryption based on AES 128, the hash value of the data file can be utilised to provide an integrity check. As a result, the PBC mode has a better speed performance while retaining the confidentiality and security provided by the CBC mode. Chapter 6 applies TPAKE and PBC to eHealth clouds. Related work on security, privacy preservation and disaster recovery are reviewed. Next, two approaches focusing on security preservation and privacy preservation, and a disaster recovery plan are proposed. The security preservation approach is a robust means of ensuring the security and integrity of electronic health records and is based on the PBC mode, while the privacy preservation approach is an efficient authentication method which protects the privacy of personal health records and is based on the TPAKE protocol. A discussion about how these integrated approaches and the disaster recovery plan can ensure the reliability and security of cloud projects follows. Distributed denial of service (DDoS) attacks are the second most common cybercrime attacks after information theft. The timely detection and prevention of such attacks in cloud projects are therefore vital, especially for eHealth clouds. Chapter 7 presents a new classification system for detecting and preventing DDoS TCP flood attacks (CS_DDoS) for public clouds, particularly in an eHealth cloud environment. The proposed CS_DDoS system offers a solution for securing stored records by classifying incoming packets and making a decision based on these classification results. During the detection phase, CS_DDOS identifies and determines whether a packet is normal or from an attacker. During the prevention phase, packets classified as malicious are denied access to the cloud service, and the source IP is blacklisted. The performance of the CS_DDoS system is compared using four different classifiers: a least-squares support vector machine (LS-SVM), naïve Bayes, K-nearest-neighbour, and multilayer perceptron. The results show that CS_DDoS yields the best performance when the LS-SVM classifier is used. This combination can detect DDoS TCP flood attacks with an accuracy of approximately 97% and a Kappa coefficient of 0.89 when under attack from a single source, and 94% accuracy and a Kappa coefficient of 0.9 when under attack from multiple attackers. These results are then discussed in terms of the accuracy and time complexity, and are validated using a k-fold cross-validation model. Finally, a method to mitigate DoS attacks in the cloud and reduce excessive energy consumption through managing and limiting certain flows of packets is proposed. Instead of a system shutdown, the proposed method ensures the availability of service. The proposed method manages the incoming packets more effectively by dropping packets from the most frequent requesting sources. This method can process 98.4% of the accepted packets during an attack. Practicality and effectiveness are essential requirements of methods for preserving the privacy and security of data in clouds. The proposed methods successfully secure cloud projects and ensure the availability of services in an efficient way

A patient agent controlled customized blockchain based framework for internet of things

Although Blockchain implementations have emerged as revolutionary technologies for various industrial applications including cryptocurrencies, they have not been widely deployed to store data streaming from sensors to remote servers in architectures known as Internet of Things. New Blockchain for the Internet of Things models promise secure solutions for eHealth, smart cities, and other applications. These models pave the way for continuous monitoring of patient’s physiological signs with wearable sensors to augment traditional medical practice without recourse to storing data with a trusted authority. However, existing Blockchain algorithms cannot accommodate the huge volumes, security, and privacy requirements of health data. In this thesis, our first contribution is an End-to-End secure eHealth architecture that introduces an intelligent Patient Centric Agent. The Patient Centric Agent executing on dedicated hardware manages the storage and access of streams of sensors generated health data, into a customized Blockchain and other less secure repositories. As IoT devices cannot host Blockchain technology due to their limited memory, power, and computational resources, the Patient Centric Agent coordinates and communicates with a private customized Blockchain on behalf of the wearable devices. While the adoption of a Patient Centric Agent offers solutions for addressing continuous monitoring of patients’ health, dealing with storage, data privacy and network security issues, the architecture is vulnerable to Denial of Services(DoS) and single point of failure attacks. To address this issue, we advance a second contribution; a decentralised eHealth system in which the Patient Centric Agent is replicated at three levels: Sensing Layer, NEAR Processing Layer and FAR Processing Layer. The functionalities of the Patient Centric Agent are customized to manage the tasks of the three levels. Simulations confirm protection of the architecture against DoS attacks. Few patients require all their health data to be stored in Blockchain repositories but instead need to select an appropriate storage medium for each chunk of data by matching their personal needs and preferences with features of candidate storage mediums. Motivated by this context, we advance third contribution; a recommendation model for health data storage that can accommodate patient preferences and make storage decisions rapidly, in real-time, even with streamed data. The mapping between health data features and characteristics of each repository is learned using machine learning. The Blockchain’s capacity to make transactions and store records without central oversight enables its application for IoT networks outside health such as underwater IoT networks where the unattended nature of the nodes threatens their security and privacy. However, underwater IoT differs from ground IoT as acoustics signals are the communication media leading to high propagation delays, high error rates exacerbated by turbulent water currents. Our fourth contribution is a customized Blockchain leveraged framework with the model of Patient-Centric Agent renamed as Smart Agent for securely monitoring underwater IoT. Finally, the smart Agent has been investigated in developing an IoT smart home or cities monitoring framework. The key algorithms underpinning to each contribution have been implemented and analysed using simulators.Doctor of Philosoph

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

NEW SECURE SOLUTIONS FOR PRIVACY AND ACCESS CONTROL IN HEALTH INFORMATION EXCHANGE

In the current digital age, almost every healthcare organization (HCO) has moved from storing patient health records on paper to storing them electronically. Health Information Exchange (HIE) is the ability to share (or transfer) patients’ health information between different HCOs while maintaining national security standards like the Health Insurance Portability and Accountability Act (HIPAA) of 1996. Over the past few years, research has been conducted to develop privacy and access control frameworks for HIE systems. The goal of this dissertation is to address the privacy and access control concerns by building practical and efficient HIE frameworks to secure the sharing of patients’ health information. The first solution allows secure HIE among different healthcare providers while focusing primarily on the privacy of patients’ information. It allows patients to authorize a certain type of health information to be retrieved, which helps prevent any unintentional leakage of information. The privacy solution also provides healthcare providers with the capability of mutual authentication and patient authentication. It also ensures the integrity and auditability of health information being exchanged. The security and performance study for the first protocol shows that it is efficient for the purpose of HIE and offers a high level of security for such exchanges. The second framework presents a new cloud-based protocol for access control to facilitate HIE across different HCOs, employing a trapdoor hash-based proxy signature in a novel manner to enable secure (authenticated and authorized) on-demand access to patient records. The proposed proxy signature-based scheme provides an explicit mechanism for patients to authorize the sharing of specific medical information with specific HCOs, which helps prevent any undesired or unintentional leakage of health information. The scheme also ensures that such authorizations are authentic with respect to both the HCOs and the patient. Moreover, the use of proxy signatures simplifies security auditing and the ability to obtain support for investigations by providing non-repudiation. Formal definitions, security specifications, and a detailed theoretical analysis, including correctness, security, and performance of both frameworks are provided which demonstrate the improvements upon other existing HIE systems

Privacy Enhancing Technologies for solving the privacy-personalization paradox : taxonomy and survey

Personal data are often collected and processed in a decentralized fashion, within different contexts. For instance, with the emergence of distributed applications, several providers are usually correlating their records, and providing personalized services to their clients. Collected data include geographical and indoor positions of users, their movement patterns as well as sensor-acquired data that may reveal users’ physical conditions, habits and interests. Consequently, this may lead to undesired consequences such as unsolicited advertisement and even to discrimination and stalking. To mitigate privacy threats, several techniques emerged, referred to as Privacy Enhancing Technologies, PETs for short. On one hand, the increasing pressure on service providers to protect users’ privacy resulted in PETs being adopted. One the other hand, service providers have built their business model on personalized services, e.g. targeted ads and news. The objective of the paper is then to identify which of the PETs have the potential to satisfy both usually divergent - economical and ethical - purposes. This paper identifies a taxonomy classifying eight categories of PETs into three groups, and for better clarity, it considers three categories of personalized services. After defining and presenting the main features of PETs with illustrative examples, the paper points out which PETs best fit each personalized service category. Then, it discusses some of the inter-disciplinary privacy challenges that may slow down the adoption of these techniques, namely: technical, social, legal and economic concerns. Finally, it provides recommendations and highlights several research directions

Advances in Information Security and Privacy

With the recent pandemic emergency, many people are spending their days in smart working and have increased their use of digital resources for both work and entertainment. The result is that the amount of digital information handled online is dramatically increased, and we can observe a significant increase in the number of attacks, breaches, and hacks. This Special Issue aims to establish the state of the art in protecting information by mitigating information risks. This objective is reached by presenting both surveys on specific topics and original approaches and solutions to specific problems. In total, 16 papers have been published in this Special Issue

Data Service Outsourcing and Privacy Protection in Mobile Internet

Mobile Internet data have the characteristics of large scale, variety of patterns, and complex association. On the one hand, it needs efficient data processing model to provide support for data services, and on the other hand, it needs certain computing resources to provide data security services. Due to the limited resources of mobile terminals, it is impossible to complete large-scale data computation and storage. However, outsourcing to third parties may cause some risks in user privacy protection. This monography focuses on key technologies of data service outsourcing and privacy protection, including the existing methods of data analysis and processing, the fine-grained data access control through effective user privacy protection mechanism, and the data sharing in the mobile Internet