42 research outputs found
Big Data Security (Volume 3)
After a short description of the key concepts of big data the book explores on the secrecy and security threats posed especially by cloud based data storage. It delivers conceptual frameworks and models along with case studies of recent technology
Data Exfiltration:A Review of External Attack Vectors and Countermeasures
AbstractContext One of the main targets of cyber-attacks is data exfiltration, which is the leakage of sensitive or private data to an unauthorized entity. Data exfiltration can be perpetrated by an outsider or an insider of an organization. Given the increasing number of data exfiltration incidents, a large number of data exfiltration countermeasures have been developed. These countermeasures aim to detect, prevent, or investigate exfiltration of sensitive or private data. With the growing interest in data exfiltration, it is important to review data exfiltration attack vectors and countermeasures to support future research in this field. Objective This paper is aimed at identifying and critically analysing data exfiltration attack vectors and countermeasures for reporting the status of the art and determining gaps for future research. Method We have followed a structured process for selecting 108 papers from seven publication databases. Thematic analysis method has been applied to analyse the extracted data from the reviewed papers. Results We have developed a classification of (1) data exfiltration attack vectors used by external attackers and (2) the countermeasures in the face of external attacks. We have mapped the countermeasures to attack vectors. Furthermore, we have explored the applicability of various countermeasures for different states of data (i.e., in use, in transit, or at rest). Conclusion This review has revealed that (a) most of the state of the art is focussed on preventive and detective countermeasures and significant research is required on developing investigative countermeasures that are equally important; (b) Several data exfiltration countermeasures are not able to respond in real-time, which specifies that research efforts need to be invested to enable them to respond in real-time (c) A number of data exfiltration countermeasures do not take privacy and ethical concerns into consideration, which may become an obstacle in their full adoption (d) Existing research is primarily focussed on protecting data in ‘in use’ state, therefore, future research needs to be directed towards securing data in ‘in rest’ and ‘in transit’ states (e) There is no standard or framework for evaluation of data exfiltration countermeasures. We assert the need for developing such an evaluation framework
Green ICT for better human life
Abstract—Seven decades of rapid development in computing and ICT make it an efficient and effective driving force toward better human life. ICT industry has an appreciated contribution to the global economy associating with innovation, invention and rapid development of almost all the aspect of human life (Education, Health, Industry, Entertainment, Agriculture, Business, etc.). On the other hand, global environment and human life facing serious challenges related to human health and life style, climate change and global warming, and unwise consumption and management of resources. The diversity and rapid increasing of ICT usage in our life leads to more energy consumption and environmental problems, which has negative impact on economy, human health, and life style. The expected ICT consumption of energy for the next few years will be about 15% of the total consumption worldwide. This make ICT industry shared responsibility for global CO2 emissions and environmental problems. Therefore, many developed countries are establishing Green ICT policies and strategies to eliminate environmental and human health problems. Shortage and weakness of Green ICT policies or strategies in developing countries requires adoption of an effective one that leads to wise ICT usage and energy consumption. Ethical and moral values should integrate with technical aspects to have effective strategies for green ICT that leads to better human life
Data Hiding and Its Applications
Data hiding techniques have been widely used to provide copyright protection, data integrity, covert communication, non-repudiation, and authentication, among other applications. In the context of the increased dissemination and distribution of multimedia content over the internet, data hiding methods, such as digital watermarking and steganography, are becoming increasingly relevant in providing multimedia security. The goal of this book is to focus on the improvement of data hiding algorithms and their different applications (both traditional and emerging), bringing together researchers and practitioners from different research fields, including data hiding, signal processing, cryptography, and information theory, among others
A Comprehensive Review on Medical Image Steganography Based on LSB Technique and Potential Challenges
The rapid development of telemedicine services and the requirements for exchanging medical information between physicians, consultants, and health institutions have made the protection of patients’ information an important priority for any future e-health system. The protection of medical information, including the cover (i.e. medical image), has a specificity that slightly differs from the requirements for protecting other information. It is necessary to preserve the cover greatly due to its importance on the reception side as medical staff use this information to provide a diagnosis to save a patient's life. If the cover is tampered with, this leads to failure in achieving the goal of telemedicine. Therefore, this work provides an investigation of information security techniques in medical imaging, focusing on security goals. Encrypting a message before hiding them gives an extra layer of security, and thus, will provide an excellent solution to protect the sensitive information of patients during the sharing of medical information. Medical image steganography is a special case of image steganography, while Digital Imaging and Communications in Medicine (DICOM) is the backbone of all medical imaging divisions, whereby it is most broadly used to store and transmit medical images. The main objective of this study is to provide a general idea of what Least Significant Bit-based (LSB) steganography techniques have achieved in medical images
인공지능 보안
학위논문 (박사) -- 서울대학교 대학원 : 자연과학대학 협동과정 생물정보학전공, 2021. 2. 윤성로.With the development of machine learning (ML), expectations for artificial intelligence (AI) technologies have increased daily. In particular, deep neural networks have demonstrated outstanding performance in many fields. However, if a deep-learning (DL) model causes mispredictions or misclassifications, it can cause difficulty, owing to malicious external influences.
This dissertation discusses DL security and privacy issues and proposes methodologies for security and privacy attacks. First, we reviewed security attacks and defenses from two aspects. Evasion attacks use adversarial examples to disrupt the classification process, and poisoning attacks compromise training by compromising the training data. Next, we reviewed attacks on privacy that can exploit exposed training data and defenses, including differential privacy and encryption.
For adversarial DL, we study the problem of finding adversarial examples against ML-based portable document format (PDF) malware classifiers. We believe that our problem is more challenging than those against ML models for image processing, owing to the highly complex data structure of PDFs, compared with traditional image datasets, and the requirement that the infected PDF should exhibit malicious behavior without being detected. We propose an attack using generative adversarial networks that effectively generates evasive PDFs using a variational autoencoder robust against adversarial examples.
For privacy in DL, we study the problem of avoiding sensitive data being misused and propose a privacy-preserving framework for deep neural networks. Our methods are based on generative models that preserve the privacy of sensitive data while maintaining a high prediction performance. Finally, we study the security aspect in biological domains to detect maliciousness in deoxyribonucleic acid sequences and watermarks to protect intellectual properties.
In summary, the proposed DL models for security and privacy embrace a diversity of research by attempting actual attacks and defenses in various fields.인공지능 모델을 사용하기 위해서는 개인별 데이터 수집이 필수적이다. 반면 개인의 민감한 데이터가 유출되는 경우에는 프라이버시 침해의 소지가 있다. 인공지능 모델을 사용하는데 수집된 데이터가 외부에 유출되지 않도록 하거나, 익명화, 부호화 등의 보안 기법을 인공지능 모델에 적용하는 분야를 Private AI로 분류할 수 있다. 또한 인공지능 모델이 노출될 경우 지적 소유권이 무력화될 수 있는 문제점과, 악의적인 학습 데이터를 이용하여 인공지능 시스템을 오작동할 수 있고 이러한 인공지능 모델 자체에 대한 위협은 Secure AI로 분류할 수 있다.
본 논문에서는 학습 데이터에 대한 공격을 기반으로 신경망의 결손 사례를 보여준다. 기존의 AEs 연구들은 이미지를 기반으로 많은 연구가 진행되었다. 보다 복잡한 heterogenous한 PDF 데이터로 연구를 확장하여 generative 기반의 모델을 제안하여 공격 샘플을 생성하였다. 다음으로 이상 패턴을 보이는 샘플을 검출할 수 있는 DNA steganalysis 방어 모델을 제안한다. 마지막으로 개인 정보 보호를 위해 generative 모델 기반의 익명화 기법들을 제안한다.
요약하면 본 논문은 인공지능 모델을 활용한 공격 및 방어 알고리즘과 신경망을 활용하는데 발생되는 프라이버시 이슈를 해결할 수 있는 기계학습 알고리즘에 기반한 일련의 방법론을 제안한다.Abstract i
List of Figures vi
List of Tables xiii
1 Introduction 1
2 Background 6
2.1 Deep Learning: a brief overview . . . . . . . . . . . . . . . . . . . 6
2.2 Security Attacks on Deep Learning Models . . . . . . . . . . . . . 10
2.2.1 Evasion Attacks . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.2 Poisoning Attack . . . . . . . . . . . . . . . . . . . . . . . 20
2.3 Defense Techniques Against Deep Learning Models . . . . . . . . . 26
2.3.1 Defense Techniques against Evasion Attacks . . . . . . . . 27
2.3.2 Defense against Poisoning Attacks . . . . . . . . . . . . . . 36
2.4 Privacy issues on Deep Learning Models . . . . . . . . . . . . . . . 38
2.4.1 Attacks on Privacy . . . . . . . . . . . . . . . . . . . . . . 39
2.4.2 Defenses Against Attacks on Privacy . . . . . . . . . . . . 40
3 Attacks on Deep Learning Models 47
3.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.1.1 Threat Model . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.1.2 Portable Document Format (PDF) . . . . . . . . . . . . . . 55
3.1.3 PDF Malware Classifiers . . . . . . . . . . . . . . . . . . . 57
3.1.4 Evasion Attacks . . . . . . . . . . . . . . . . . . . . . . . 58
3.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.2.1 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . 60
3.2.2 Feature Selection Process . . . . . . . . . . . . . . . . . . 61
3.2.3 Seed Selection for Mutation . . . . . . . . . . . . . . . . . 62
3.2.4 Evading Model . . . . . . . . . . . . . . . . . . . . . . . . 63
3.2.5 Model architecture . . . . . . . . . . . . . . . . . . . . . . 67
3.2.6 PDF Repacking and Verification . . . . . . . . . . . . . . . 67
3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
3.3.1 Datasets and Model Training . . . . . . . . . . . . . . . . . 68
3.3.2 Target Classifiers . . . . . . . . . . . . . . . . . . . . . . . 71
3.3.3 CVEs for Various Types of PDF Malware . . . . . . . . . . 72
3.3.4 Malicious Signature . . . . . . . . . . . . . . . . . . . . . 72
3.3.5 AntiVirus Engines (VirusTotal) . . . . . . . . . . . . . . . 76
3.3.6 Feature Mutation Result for Contagio . . . . . . . . . . . . 76
3.3.7 Feature Mutation Result for CVEs . . . . . . . . . . . . . . 78
3.3.8 Malicious Signature Verification . . . . . . . . . . . . . . . 78
3.3.9 Evasion Speed . . . . . . . . . . . . . . . . . . . . . . . . 80
3.3.10 AntiVirus Engines (VirusTotal) Result . . . . . . . . . . . . 82
3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
4 Defense on Deep Learning Models 88
4.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
4.1.1 Message-Hiding Regions . . . . . . . . . . . . . . . . . . . 91
4.1.2 DNA Steganography . . . . . . . . . . . . . . . . . . . . . 92
4.1.3 Example of Message Hiding . . . . . . . . . . . . . . . . . 94
4.1.4 DNA Steganalysis . . . . . . . . . . . . . . . . . . . . . . 95
4.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
4.2.1 Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
4.2.2 Proposed Model Architecture . . . . . . . . . . . . . . . . 103
4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
4.3.1 Experiment Setup . . . . . . . . . . . . . . . . . . . . . . . 105
4.3.2 Environment . . . . . . . . . . . . . . . . . . . . . . . . . 106
4.3.3 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
4.3.4 Model Training . . . . . . . . . . . . . . . . . . . . . . . . 107
4.3.5 Message Hiding Procedure . . . . . . . . . . . . . . . . . . 108
4.3.6 Evaluation Procedure . . . . . . . . . . . . . . . . . . . . . 109
4.3.7 Performance Comparison . . . . . . . . . . . . . . . . . . . 109
4.3.8 Analyzing Malicious Code in DNA Sequences . . . . . . . 112
4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
5 Privacy: Generative Models for Anonymizing Private Data 115
5.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
5.1.1 Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
5.1.2 Anonymization using GANs . . . . . . . . . . . . . . . . . 119
5.1.3 Security Principle of Anonymized GANs . . . . . . . . . . 123
5.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
5.2.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
5.2.2 Target Classifiers . . . . . . . . . . . . . . . . . . . . . . . 126
5.2.3 Model Training . . . . . . . . . . . . . . . . . . . . . . . . 126
5.2.4 Evaluation Process . . . . . . . . . . . . . . . . . . . . . . 126
5.2.5 Comparison to Differential Privacy . . . . . . . . . . . . . 128
5.2.6 Performance Comparison . . . . . . . . . . . . . . . . . . . 128
5.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
6 Privacy: Privacy-preserving Inference for Deep Learning Models 132
6.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
6.1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . 135
6.1.2 Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
6.1.3 Deep Private Generation Framework . . . . . . . . . . . . . 137
6.1.4 Security Principle . . . . . . . . . . . . . . . . . . . . . . . 141
6.1.5 Threat to the Classifier . . . . . . . . . . . . . . . . . . . . 143
6.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
6.2.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
6.2.2 Experimental Process . . . . . . . . . . . . . . . . . . . . . 146
6.2.3 Target Classifiers . . . . . . . . . . . . . . . . . . . . . . . 147
6.2.4 Model Training . . . . . . . . . . . . . . . . . . . . . . . . 147
6.2.5 Model Evaluation . . . . . . . . . . . . . . . . . . . . . . . 149
6.2.6 Performance Comparison . . . . . . . . . . . . . . . . . . . 150
6.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
7 Conclusion 153
7.0.1 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . 154
7.0.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . 155
Bibliography 157
Abstract in Korean 195Docto
Fuzzy Logic
The capability of Fuzzy Logic in the development of emerging technologies is introduced in this book. The book consists of sixteen chapters showing various applications in the field of Bioinformatics, Health, Security, Communications, Transportations, Financial Management, Energy and Environment Systems. This book is a major reference source for all those concerned with applied intelligent systems. The intended readers are researchers, engineers, medical practitioners, and graduate students interested in fuzzy logic systems
Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection
Cyber attacks are currently blooming, as the attackers reap significant profits from them and face a limited risk when compared to committing the "classical" crimes. One of the major components that leads to the successful compromising of the targeted system is malicious software. It allows using the victim's machine for various nefarious purposes, e.g., making it a part of the botnet, mining cryptocurrencies, or holding hostage the data stored there. At present, the complexity, proliferation, and variety of malware pose a real challenge for the existing countermeasures and require their constant improvements. That is why, in this paper we first perform a detailed meta-review of the existing surveys related to malware and its detection techniques, showing an arms race between these two sides of a barricade. On this basis, we review the evolution of modern threats in the communication networks, with a particular focus on the techniques employing information hiding. Next, we present the bird's eye view portraying the main development trends in detection methods with a special emphasis on the machine learning techniques. The survey is concluded with the description of potential future research directions in the field of malware detection
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