Designing Novel Hardware Security Primitives for Smart Computing Devices

Smart computing devices are miniaturized electronics devices that can sense their surroundings, communicate, and share information autonomously with other devices to work cohesively. Smart devices have played a major role in improving quality of the life and boosting the global economy. They are ubiquitously present, smart home, smart city, smart girds, industry, healthcare, controlling the hazardous environment, and military, etc. However, we have witnessed an exponential rise in potential threat vectors and physical attacks in recent years. The conventional software-based security approaches are not suitable in the smart computing device, therefore, hardware-enabled security solutions have emerged as an attractive choice. Developing hardware security primitives, such as True Random Number Generator (TRNG) and Physically Unclonable Function (PUF) from electrical properties of the sensor could be a novel research direction. Secondly, the Lightweight Cryptographic (LWC) ciphers used in smart computing devices are found vulnerable against Correlation Power Analysis (CPA) attack. The CPA performs statistical analysis of the power consumption of the cryptographic core and reveals the encryption key. The countermeasure against CPA results in an increase in energy consumption, therefore, they are not suitable for battery operated smart computing devices. The primary goal of this dissertation is to develop novel hardware security primitives from existing sensors and energy-efficient LWC circuit implementation with CPA resilience. To achieve these. we focus on developing TRNG and PUF from existing photoresistor and photovoltaic solar cell sensors in smart devices Further, we explored energy recovery computing (also known as adiabatic computing) circuit design technique that reduces the energy consumption compared to baseline CMOS logic design and same time increasing CPA resilience in low-frequency applications, e.g. wearable fitness gadgets, hearing aid and biomedical instruments. The first contribution of this dissertation is to develop a TRNG prototype from the uncertainty present in photoresistor sensors. The existing sensor-based TRNGs suffer a low random bit generation rate, therefore, are not suitable in real-time applications. The proposed prototype has an average random bit generation rate of 8 kbps, 32 times higher than the existing sensor-based TRNG. The proposed lightweight scrambling method results in random bit entropy close to ideal value 1. The proposed TRNG prototype passes all 15 statistical tests of the National Institute of Standards and Technology (NIST) Statistical Test Suite with quality performance. The second contribution of this dissertation is to develop an integrated TRNG-PUF designed using photovoltaic solar cell sensors. The TRNG and PUF are mutually independent in the way they are designed, therefore, integrating them as one architecture can be beneficial in resource-constrained computing devices. We propose a novel histogram-based technique to segregate photovoltaic solar cell sensor response suitable for TRNG and PUF respectively. The proposed prototype archives approximately 34\% improvement in TRNG output. The proposed prototype achieves an average of 92.13\% reliability and 50.91\% uniformity performance in PUF response. The proposed sensor-based hardware security primitives do not require additional interfacing hardware. Therefore, they can be ported as a software update on existing photoresistor and photovoltaic sensor-based devices. Furthermore, the sensor-based design approach can identify physically tempered and faulty sensor nodes during authentication as their response bit differs. The third contribution is towards the development of a novel 2-phase sinusoidal clocking implementation, 2-SPGAL for existing Symmetric Pass Gate Adiabatic Logic (SPGAL). The proposed 2-SPGAL logic-based LWC cipher PRESENT shows an average of 49.34\% energy saving compared to baseline CMOS logic implementation. Furthermore, the 2-SPGAL prototype has an average of 22.76\% better energy saving compared to 2-EE-SPFAL (2-phase Energy-Efficient-Secure Positive Feedback Adiabatic Logic). The proposed 2-SPGAL was tested for energy-efficiency performance for the frequency range of 50 kHz to 250 kHz, used in healthcare gadgets and biomedical instruments. The proposed 2-SPGAL based design saves 16.78\% transistor count compared to 2-EE-SPFAL counterpart. The final contribution is to explore Clocked CMOS Adiabatic Logic (CCAL) to design a cryptographic circuit. Previously proposed 2-SPGAL and 2-EE-SPFAL uses two complementary pairs of the transistor evaluation network, thus resulting in a higher transistor count compared to the CMOS counterpart. The CCAL structure is very similar to CMOS and unlike 2-SPGAL and 2-EE-SPFAL, it does not require discharge circuitry to improve security performance. The case-study implementation LWC cipher PRESENT S-Box using CCAL results into 45.74\% and 34.88\% transistor count saving compared to 2-EE-SPFAL and 2-SPGAL counterpart. Furthermore, the case-study implementation using CCAL shows more than 95\% energy saving compared to CMOS logic at frequency range 50 kHz to 125 kHz, and approximately 60\% energy saving at frequency 250 kHz. The case study also shows 32.67\% and 11.21\% more energy saving compared to 2-EE-SPFAL and 2-SPGAL respectively at frequency 250 kHz. We also show that 200 fF of tank capacitor in the clock generator circuit results in optimum energy and security performance in CCAL

Muscle activity-driven green-oriented random number generation mechanism to secure WBSN wearable device communications

Wireless body sensor networks (WBSNs) mostly consist of low-cost sensor nodes and implanted devices which generally have extremely limited capability of computations and energy capabilities. Hence, traditional security protocols and privacy enhancing technologies are not applicable to the WBSNs since their computations and cryptographic primitives are normally exceedingly complicated. Nowadays, mobile wearable and wireless muscle-computer interfaces have been integrated with the WBSN sensors for various applications such as rehabilitation, sports, entertainment, and healthcare. In this paper, we propose MGRNG, a novel muscle activity-driven green-oriented random number generation mechanism which uses the human muscle activity as green energy resource to generate random numbers (RNs). The RNs can be used to enhance the privacy of wearable device communications and secure WBSNs for rehabilitation purposes. The method was tested on 10 healthy subjects as well as 5 amputee subjects with 105 segments of simultaneously recorded surface electromyography signals from their forearm muscles. The proposed MGRNG requires only one second to generate a 128-bit RN, which is much more efficient when compared to the electrocardiography-based RN generation algorithms. Experimental results show that the RNs generated from human muscle activity signals can pass the entropy test and the NIST random test and thus can be used to secure the WBSN nodes

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

Privacy-aware Security Applications in the Era of Internet of Things

In this dissertation, we introduce several novel privacy-aware security applications. We split these contributions into three main categories: First, to strengthen the current authentication mechanisms, we designed two novel privacy-aware alternative complementary authentication mechanisms, Continuous Authentication (CA) and Multi-factor Authentication (MFA). Our first system is Wearable-assisted Continuous Authentication (WACA), where we used the sensor data collected from a wrist-worn device to authenticate users continuously. Then, we improved WACA by integrating a noise-tolerant template matching technique called NTT-Sec to make it privacy-aware as the collected data can be sensitive. We also designed a novel, lightweight, Privacy-aware Continuous Authentication (PACA) protocol. PACA is easily applicable to other biometric authentication mechanisms when feature vectors are represented as fixed-length real-valued vectors. In addition to CA, we also introduced a privacy-aware multi-factor authentication method, called PINTA. In PINTA, we used fuzzy hashing and homomorphic encryption mechanisms to protect the users\u27 sensitive profiles while providing privacy-preserving authentication. For the second privacy-aware contribution, we designed a multi-stage privacy attack to smart home users using the wireless network traffic generated during the communication of the devices. The attack works even on the encrypted data as it is only using the metadata of the network traffic. Moreover, we also designed a novel solution based on the generation of spoofed traffic. Finally, we introduced two privacy-aware secure data exchange mechanisms, which allow sharing the data between multiple parties (e.g., companies, hospitals) while preserving the privacy of the individual in the dataset. These mechanisms were realized with the combination of Secure Multiparty Computation (SMC) and Differential Privacy (DP) techniques. In addition, we designed a policy language, called Curie Policy Language (CPL), to handle the conflicting relationships among parties. The novel methods, attacks, and countermeasures in this dissertation were verified with theoretical analysis and extensive experiments with real devices and users. We believe that the research in this dissertation has far-reaching implications on privacy-aware alternative complementary authentication methods, smart home user privacy research, as well as the privacy-aware and secure data exchange methods

Image-based Authentication

Mobile and wearable devices are popular platforms for accessing online services. However, the small form factor of such devices, makes a secure and practical experience for user authentication, challenging. Further, online fraud that includes phishing attacks, has revealed the importance of conversely providing solutions for usable authentication of remote services to online users. In this thesis, we introduce image-based solutions for mutual authentication between a user and a remote service provider. First, we propose and develop Pixie, a two-factor, object-based authentication solution for camera-equipped mobile and wearable devices. We further design ai.lock, a system that reliably extracts from images, authentication credentials similar to biometrics. Second, we introduce CEAL, a system to generate visual key fingerprint representations of arbitrary binary strings, to be used to visually authenticate online entities and their cryptographic keys. CEAL leverages deep learning to capture the target style and domain of training images, into a generator model from a large collection of sample images rather than hand curated as a collection of rules, hence provides a unique capacity for easy customizability. CEAL integrates a model of the visual discriminative ability of human perception, hence the resulting fingerprint image generator avoids mapping distinct keys to images which are not distinguishable by humans. Further, CEAL deterministically generates visually pleasing fingerprint images from an input vector where the vector components are designated to represent visual properties which are either readily perceptible to human eye, or imperceptible yet are necessary for accurately modeling the target image domain. We show that image-based authentication using Pixie is usable and fast, while ai.lock extracts authentication credentials that exceed the entropy of biometrics. Further, we show that CEAL outperforms state-of-the-art solution in terms of efficiency, usability, and resilience to powerful adversarial attacks

Design of a secure architecture for the exchange of biomedical information in m-Health scenarios

El paradigma de m-Salud (salud móvil) aboga por la integración masiva de las más avanzadas tecnologías de comunicación, red móvil y sensores en aplicaciones y sistemas de salud, para fomentar el despliegue de un nuevo modelo de atención clínica centrada en el usuario/paciente. Este modelo tiene por objetivos el empoderamiento de los usuarios en la gestión de su propia salud (p.ej. aumentando sus conocimientos, promocionando estilos de vida saludable y previniendo enfermedades), la prestación de una mejor tele-asistencia sanitaria en el hogar para ancianos y pacientes crónicos y una notable disminución del gasto de los Sistemas de Salud gracias a la reducción del número y la duración de las hospitalizaciones. No obstante, estas ventajas, atribuidas a las aplicaciones de m-Salud, suelen venir acompañadas del requisito de un alto grado de disponibilidad de la información biomédica de sus usuarios para garantizar una alta calidad de servicio, p.ej. fusionar varias señales de un usuario para obtener un diagnóstico más preciso. La consecuencia negativa de cumplir esta demanda es el aumento directo de las superficies potencialmente vulnerables a ataques, lo que sitúa a la seguridad (y a la privacidad) del modelo de m-Salud como factor crítico para su éxito. Como requisito no funcional de las aplicaciones de m-Salud, la seguridad ha recibido menos atención que otros requisitos técnicos que eran más urgentes en etapas de desarrollo previas, tales como la robustez, la eficiencia, la interoperabilidad o la usabilidad. Otro factor importante que ha contribuido a retrasar la implementación de políticas de seguridad sólidas es que garantizar un determinado nivel de seguridad implica unos costes que pueden ser muy relevantes en varias dimensiones, en especial en la económica (p.ej. sobrecostes por la inclusión de hardware extra para la autenticación de usuarios), en el rendimiento (p.ej. reducción de la eficiencia y de la interoperabilidad debido a la integración de elementos de seguridad) y en la usabilidad (p.ej. configuración más complicada de dispositivos y aplicaciones de salud debido a las nuevas opciones de seguridad). Por tanto, las soluciones de seguridad que persigan satisfacer a todos los actores del contexto de m-Salud (usuarios, pacientes, personal médico, personal técnico, legisladores, fabricantes de dispositivos y equipos, etc.) deben ser robustas y al mismo tiempo minimizar sus costes asociados. Esta Tesis detalla una propuesta de seguridad, compuesta por cuatro grandes bloques interconectados, para dotar de seguridad a las arquitecturas de m-Salud con unos costes reducidos. El primer bloque define un esquema global que proporciona unos niveles de seguridad e interoperabilidad acordes con las características de las distintas aplicaciones de m-Salud. Este esquema está compuesto por tres capas diferenciadas, diseñadas a la medidas de los dominios de m-Salud y de sus restricciones, incluyendo medidas de seguridad adecuadas para la defensa contra las amenazas asociadas a sus aplicaciones de m-Salud. El segundo bloque establece la extensión de seguridad de aquellos protocolos estándar que permiten la adquisición, el intercambio y/o la administración de información biomédica -- por tanto, usados por muchas aplicaciones de m-Salud -- pero no reúnen los niveles de seguridad detallados en el esquema previo. Estas extensiones se concretan para los estándares biomédicos ISO/IEEE 11073 PHD y SCP-ECG. El tercer bloque propone nuevas formas de fortalecer la seguridad de los tests biomédicos, que constituyen el elemento esencial de muchas aplicaciones de m-Salud de carácter clínico, mediante codificaciones novedosas. Finalmente el cuarto bloque, que se sitúa en paralelo a los anteriores, selecciona herramientas genéricas de seguridad (elementos de autenticación y criptográficos) cuya integración en los otros bloques resulta idónea, y desarrolla nuevas herramientas de seguridad, basadas en señal -- embedding y keytagging --, para reforzar la protección de los test biomédicos.The paradigm of m-Health (mobile health) advocates for the massive integration of advanced mobile communications, network and sensor technologies in healthcare applications and systems to foster the deployment of a new, user/patient-centered healthcare model enabling the empowerment of users in the management of their health (e.g. by increasing their health literacy, promoting healthy lifestyles and the prevention of diseases), a better home-based healthcare delivery for elderly and chronic patients and important savings for healthcare systems due to the reduction of hospitalizations in number and duration. It is a fact that many m-Health applications demand high availability of biomedical information from their users (for further accurate analysis, e.g. by fusion of various signals) to guarantee high quality of service, which on the other hand entails increasing the potential surfaces for attacks. Therefore, it is not surprising that security (and privacy) is commonly included among the most important barriers for the success of m-Health. As a non-functional requirement for m-Health applications, security has received less attention than other technical issues that were more pressing at earlier development stages, such as reliability, eficiency, interoperability or usability. Another fact that has contributed to delaying the enforcement of robust security policies is that guaranteeing a certain security level implies costs that can be very relevant and that span along diferent dimensions. These include budgeting (e.g. the demand of extra hardware for user authentication), performance (e.g. lower eficiency and interoperability due to the addition of security elements) and usability (e.g. cumbersome configuration of devices and applications due to security options). Therefore, security solutions that aim to satisfy all the stakeholders in the m-Health context (users/patients, medical staff, technical staff, systems and devices manufacturers, regulators, etc.) shall be robust and, at the same time, minimize their associated costs. This Thesis details a proposal, composed of four interrelated blocks, to integrate appropriate levels of security in m-Health architectures in a cost-efcient manner. The first block designes a global scheme that provides different security and interoperability levels accordingto how critical are the m-Health applications to be implemented. This consists ofthree layers tailored to the m-Health domains and their constraints, whose security countermeasures defend against the threats of their associated m-Health applications. Next, the second block addresses the security extension of those standard protocols that enable the acquisition, exchange and/or management of biomedical information | thus, used by many m-Health applications | but do not meet the security levels described in the former scheme. These extensions are materialized for the biomedical standards ISO/IEEE 11073 PHD and SCP-ECG. Then, the third block proposes new ways of enhancing the security of biomedical standards, which are the centerpiece of many clinical m-Health applications, by means of novel codings. Finally the fourth block, with is parallel to the others, selects generic security methods (for user authentication and cryptographic protection) whose integration in the other blocks results optimal, and also develops novel signal-based methods (embedding and keytagging) for strengthening the security of biomedical tests. The layer-based extensions of the standards ISO/IEEE 11073 PHD and SCP-ECG can be considered as robust, cost-eficient and respectful with their original features and contents. The former adds no attributes to its data information model, four new frames to the service model |and extends four with new sub-frames|, and only one new sub-state to the communication model. Furthermore, a lightweight architecture consisting of a personal health device mounting a 9 MHz processor and an aggregator mounting a 1 GHz processor is enough to transmit a 3-lead electrocardiogram in real-time implementing the top security layer. The extra requirements associated to this extension are an initial configuration of the health device and the aggregator, tokens for identification/authentication of users if these devices are to be shared and the implementation of certain IHE profiles in the aggregator to enable the integration of measurements in healthcare systems. As regards to the extension of SCP-ECG, it only adds a new section with selected security elements and syntax in order to protect the rest of file contents and provide proper role-based access control. The overhead introduced in the protected SCP-ECG is typically 2{13 % of the regular file size, and the extra delays to protect a newly generated SCP-ECG file and to access it for interpretation are respectively a 2{10 % and a 5 % of the regular delays. As regards to the signal-based security techniques developed, the embedding method is the basis for the proposal of a generic coding for tests composed of biomedical signals, periodic measurements and contextual information. This has been adjusted and evaluated with electrocardiogram and electroencephalogram-based tests, proving the objective clinical quality of the coded tests, the capacity of the coding-access system to operate in real-time (overall delays of 2 s for electrocardiograms and 3.3 s for electroencephalograms) and its high usability. Despite of the embedding of security and metadata to enable m-Health services, the compression ratios obtained by this coding range from ' 3 in real-time transmission to ' 5 in offline operation. Complementarily, keytagging permits associating information to images (and other signals) by means of keys in a secure and non-distorting fashion, which has been availed to implement security measures such as image authentication, integrity control and location of tampered areas, private captioning with role-based access control, traceability and copyright protection. The tests conducted indicate a remarkable robustness-capacity tradeoff that permits implementing all this measures simultaneously, and the compatibility of keytagging with JPEG2000 compression, maintaining this tradeoff while setting the overall keytagging delay in only ' 120 ms for any image size | evidencing the scalability of this technique. As a general conclusion, it has been demonstrated and illustrated with examples that there are various, complementary and structured manners to contribute in the implementation of suitable security levels for m-Health architectures with a moderate cost in budget, performance, interoperability and usability. The m-Health landscape is evolving permanently along all their dimensions, and this Thesis aims to do so with its security. Furthermore, the lessons learned herein may offer further guidance for the elaboration of more comprehensive and updated security schemes, for the extension of other biomedical standards featuring low emphasis on security or privacy, and for the improvement of the state of the art regarding signal-based protection methods and applications

Recent Application in Biometrics

In the recent years, a number of recognition and authentication systems based on biometric measurements have been proposed. Algorithms and sensors have been developed to acquire and process many different biometric traits. Moreover, the biometric technology is being used in novel ways, with potential commercial and practical implications to our daily activities. The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in biometrics. The topics covered in this book reflect well both aspects of development. They include biometric sample quality, privacy preserving and cancellable biometrics, contactless biometrics, novel and unconventional biometrics, and the technical challenges in implementing the technology in portable devices. The book consists of 15 chapters. It is divided into four sections, namely, biometric applications on mobile platforms, cancelable biometrics, biometric encryption, and other applications. The book was reviewed by editors Dr. Jucheng Yang and Dr. Norman Poh. We deeply appreciate the efforts of our guest editors: Dr. Girija Chetty, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park and Dr. Sook Yoon, as well as a number of anonymous reviewers

Analytics over Encrypted Traffic and Defenses

Encrypted traffic flows have been known to leak information about their underlying content through statistical properties such as packet lengths and timing. While traffic fingerprinting attacks exploit such information leaks and threaten user privacy by disclosing website visits, videos streamed, and user activity on messaging platforms, they can also be helpful in network management and intelligence services. Most recent and best-performing such attacks are based on deep learning models. In this thesis, we identify multiple limitations in the currently available attacks and defenses against them. First, these deep learning models do not provide any insights into their decision-making process. Second, most attacks that have achieved very high accuracies are still limited by unrealistic assumptions that affect their practicality. For example, most attacks assume a closed world setting and focus on traffic classification after event completion. Finally, current state-of-the-art defenses still incur high overheads to provide reasonable privacy, which limits their applicability in real-world applications. In order to address these limitations, we first propose an inline traffic fingerprinting attack based on variable-length sequence modeling to facilitate real-time analytics. Next, we attempt to understand the inner workings of deep learning-based attacks with the dual goals of further improving attacks and designing efficient defenses against such attacks. Then, based on the observations from this analysis, we propose two novel defenses against traffic fingerprinting attacks that provide privacy under more realistic constraints and at lower bandwidth overheads. Finally, we propose a robust framework for open set classification that targets network traffic with this added advantage of being more suitable for deployment in resource-constrained in-network devices

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

Improving Access and Mental Health for Youth Through Virtual Models of Care

The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14–25 years, with symptoms of anxiety/depression. This project includes 115 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using eHealth technology to enhance care. The technology uses mobile questionnaires to help promote self-assessment and track changes to support the plan of care. The technology also allows secure virtual treatment visits that youth can participate in through mobile devices. This longitudinal study uses participatory action research with mixed methods. The majority of participants identified themselves as Caucasian (66.9%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (71.9% and 67.5% respectively). Findings from the qualitative summary established that both staff and youth found the software and platform beneficial