Towards end-to-end security in internet of things based healthcare

Healthcare IoT systems are distinguished in that they are designed to serve human beings, which primarily raises the requirements of security, privacy, and reliability. Such systems have to provide real-time notifications and responses concerning the status of patients. Physicians, patients, and other caregivers demand a reliable system in which the results are accurate and timely, and the service is reliable and secure. To guarantee these requirements, the smart components in the system require a secure and efficient end-to-end communication method between the end-points (e.g., patients, caregivers, and medical sensors) of a healthcare IoT system. The main challenge faced by the existing security solutions is a lack of secure end-to-end communication. This thesis addresses this challenge by presenting a novel end-to-end security solution enabling end-points to securely and efficiently communicate with each other. The proposed solution meets the security requirements of a wide range of healthcare IoT systems while minimizing the overall hardware overhead of end-to-end communication. End-to-end communication is enabled by the holistic integration of the following contributions. The first contribution is the implementation of two architectures for remote monitoring of bio-signals. The first architecture is based on a low power IEEE 802.15.4 protocol known as ZigBee. It consists of a set of sensor nodes to read data from various medical sensors, process the data, and send them wirelessly over ZigBee to a server node. The second architecture implements on an IP-based wireless sensor network, using IEEE 802.11 Wireless Local Area Network (WLAN). The system consists of a IEEE 802.11 based sensor module to access bio-signals from patients and send them over to a remote server. In both architectures, the server node collects the health data from several client nodes and updates a remote database. The remote webserver accesses the database and updates the webpage in real-time, which can be accessed remotely. The second contribution is a novel secure mutual authentication scheme for Radio Frequency Identification (RFID) implant systems. The proposed scheme relies on the elliptic curve cryptography and the D-Quark lightweight hash design. The scheme consists of three main phases: (1) reader authentication and verification, (2) tag identification, and (3) tag verification. We show that among the existing public-key crypto-systems, elliptic curve is the optimal choice due to its small key size as well as its efficiency in computations. The D-Quark lightweight hash design has been tailored for resource-constrained devices. The third contribution is proposing a low-latency and secure cryptographic keys generation approach based on Electrocardiogram (ECG) features. This is performed by taking advantage of the uniqueness and randomness properties of ECG's main features comprising of PR, RR, PP, QT, and ST intervals. This approach achieves low latency due to its reliance on reference-free ECG's main features that can be acquired in a short time. The approach is called Several ECG Features (SEF)-based cryptographic key generation. The fourth contribution is devising a novel secure and efficient end-to-end security scheme for mobility enabled healthcare IoT. The proposed scheme consists of: (1) a secure and efficient end-user authentication and authorization architecture based on the certificate based Datagram Transport Layer Security (DTLS) handshake protocol, (2) a secure end-to-end communication method based on DTLS session resumption, and (3) support for robust mobility based on interconnected smart gateways in the fog layer. Finally, the fifth and the last contribution is the analysis of the performance of the state-of-the-art end-to-end security solutions in healthcare IoT systems including our end-to-end security solution. In this regard, we first identify and present the essential requirements of robust security solutions for healthcare IoT systems. We then analyze the performance of the state-of-the-art end-to-end security solutions (including our scheme) by developing a prototype healthcare IoT system

Crowdfunding Non-fungible Tokens on the Blockchain

Non-fungible tokens (NFTs) have been used as a way of rewarding content creators. Artists publish their works on the blockchain as NFTs, which they can then sell. The buyer of an NFT then holds ownership of a unique digital asset, which can be resold in much the same way that real-world art collectors might trade paintings. However, while a deal of effort has been spent on selling works of art on the blockchain, very little attention has been paid to using the blockchain as a means of fundraising to help finance the artist’s work in the first place. Additionally, while blockchains like Ethereum are ideal for smaller works of art, additional support is needed when the artwork is larger than is feasible to store on the blockchain. In this paper, we propose a fundraising mechanism that will help artists to gain financial support for their initiatives, and where the backers can receive a share of the profits in exchange for their support. We discuss our prototype implementation using the SpartanGold framework. We then discuss how this system could be expanded to support large NFTs with the 0Chain blockchain, and describe how we could provide support for ongoing storage of these NFTs

Fake Malware Generation Using HMM and GAN

In the past decade, the number of malware attacks have grown considerably and, more importantly, evolved. Many researchers have successfully integrated state-of-the-art machine learning techniques to combat this ever present and rising threat to information security. However, the lack of enough data to appropriately train these machine learning models is one big challenge that is still present. Generative modelling has proven to be very efficient at generating image-like synthesized data that can match the actual data distribution. In this paper, we aim to generate malware samples as opcode sequences and attempt to differentiate them from the real ones with the goal to build fake malware data that can be used to effectively train the machine learning models. We use and compare different Generative Adversarial Networks (GAN) algorithms and Hidden Markov Models (HMM) to generate such fake samples obtaining promising results

Privacy preserving linkage and sharing of sensitive data

2018 Summer.Includes bibliographical references.Sensitive data, such as personal and business information, is collected by many service providers nowadays. This data is considered as a rich source of information for research purposes that could benet individuals, researchers and service providers. However, because of the sensitivity of such data, privacy concerns, legislations, and con ict of interests, data holders are reluctant to share their data with others. Data holders typically lter out or obliterate privacy related sensitive information from their data before sharing it, which limits the utility of this data and aects the accuracy of research. Such practice will protect individuals' privacy; however it prevents researchers from linking records belonging to the same individual across dierent sources. This is commonly referred to as record linkage problem by the healthcare industry. In this dissertation, our main focus is on designing and implementing ecient privacy preserving methods that will encourage sensitive information sources to share their data with researchers without compromising the privacy of the clients or aecting the quality of the research data. The proposed solution should be scalable and ecient for real-world deploy- ments and provide good privacy assurance. While this problem has been investigated before, most of the proposed solutions were either considered as partial solutions, not accurate, or impractical, and therefore subject to further improvements. We have identied several issues and limitations in the state of the art solutions and provided a number of contributions that improve upon existing solutions. Our rst contribution is the design of privacy preserving record linkage protocol using semi-trusted third party. The protocol allows a set of data publishers (data holders) who compete with each other, to share sensitive information with subscribers (researchers) while preserving the privacy of their clients and without sharing encryption keys. Our second contribution is the design and implementation of a probabilistic privacy preserving record linkage protocol, that accommodates discrepancies and errors in the data such as typos. This work builds upon the previous work by linking the records that are similar, where the similarity range is formally dened. Our third contribution is a protocol that performs information integration and sharing without third party services. We use garbled circuits secure computation to design and build a system to perform the record linkages between two parties without sharing their data. Our design uses Bloom lters as inputs to the garbled circuits and performs a probabilistic record linkage using the Dice coecient similarity measure. As garbled circuits are known for their expensive computations, we propose new approaches that reduce the computation overhead needed, to achieve a given level of privacy. We built a scalable record linkage system using garbled circuits, that could be deployed in a distributed computation environment like the cloud, and evaluated its security and performance. One of the performance issues for linking large datasets is the amount of secure computation to compare every pair of records across the linked datasets to nd all possible record matches. To reduce the amount of computations a method, known as blocking, is used to lter out as much as possible of the record pairs that will not match, and limit the comparison to a subset of the record pairs (called can- didate pairs) that possibly match. Most of the current blocking methods either require the parties to share blocking keys (called blocks identiers), extracted from the domain of some record attributes (termed blocking variables), or share reference data points to group their records around these points using some similarity measures. Though these methods reduce the computation substantially, they leak too much information about the records within each block. Toward this end, we proposed a novel privacy preserving approximate blocking scheme that allows parties to generate the list of candidate pairs with high accuracy, while protecting the privacy of the records in each block. Our scheme is congurable such that the level of performance and accuracy could be achieved according to the required level of privacy. We analyzed the accuracy and privacy of our scheme, implemented a prototype of the scheme, and experimentally evaluated its accuracy and performance against dierent levels of privacy

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

Cryptography and Its Applications in Information Security

Nowadays, mankind is living in a cyber world. Modern technologies involve fast communication links between potentially billions of devices through complex networks (satellite, mobile phone, Internet, Internet of Things (IoT), etc.). The main concern posed by these entangled complex networks is their protection against passive and active attacks that could compromise public security (sabotage, espionage, cyber-terrorism) and privacy. This Special Issue “Cryptography and Its Applications in Information Security” addresses the range of problems related to the security of information in networks and multimedia communications and to bring together researchers, practitioners, and industrials interested by such questions. It consists of eight peer-reviewed papers, however easily understandable, that cover a range of subjects and applications related security of information

Actas de la XIII Reunión Española sobre Criptología y Seguridad de la Información RECSI XIII : Alicante, 2-5 de septiembre de 2014

Si tuviéramos que elegir un conjunto de palabras clave para definir la sociedad actual, sin duda el término información sería uno de los más representativos. Vivimos en un mundo caracterizado por un continuo flujo de información en el que las Tecnologías de la Información y Comunicación (TIC) y las Redes Sociales desempeñan un papel relevante. En la Sociedad de la Información se generan gran variedad de datos en formato digital, siendo la protección de los mismos frente a accesos y usos no autorizados el objetivo principal de lo que conocemos como Seguridad de la Información. Si bien la Criptología es una herramienta tecnológica básica, dedicada al desarrollo y análisis de sistemas y protocolos que garanticen la seguridad de los datos, el espectro de tecnologías que intervienen en la protección de la información es amplio y abarca diferentes disciplinas. Una de las características de esta ciencia es su rápida y constante evolución, motivada en parte por los continuos avances que se producen en el terreno de la computación, especialmente en las últimas décadas. Sistemas, protocolos y herramientas en general considerados seguros en la actualidad dejarán de serlo en un futuro más o menos cercano, lo que hace imprescindible el desarrollo de nuevas herramientas que garanticen, de forma eficiente, los necesarios niveles de seguridad. La Reunión Española sobre Criptología y Seguridad de la Información (RECSI) es el congreso científico español de referencia en el ámbito de la Criptología y la Seguridad en las TIC, en el que se dan cita periódicamente los principales investigadores españoles y de otras nacionalidades en esta disciplina, con el fin de compartir los resultados más recientes de su investigación. Del 2 al 5 de septiembre de 2014 se celebrará la decimotercera edición en la ciudad de Alicante, organizada por el grupo de Criptología y Seguridad Computacional de la Universidad de Alicante. Las anteriores ediciones tuvieron lugar en Palma de Mallorca (1991), Madrid (1992), Barcelona (1994), Valladolid (1996), Torremolinos (1998), Santa Cruz de Tenerife (2000), Oviedo (2002), Leganés (2004), Barcelona (2006), Salamanca (2008), Tarragona (2010) y San Sebastián (2012)