Medical Image Encryption Using Modified Identity Based Encryption

The development of technology and communication also affects the level of security needed for digital image transmission. It is known that digital images now have important meanings in both communication and video conference. In this paper, we propose a security method for medical encryption in the form of images. The proposed method is implemented in the modified Identity-Based Encryption scheme. The encryption algorithm used is Elliptic Curve Cryptography (ECC) to generate key pairs and the Advanced Encryption Standard (AES) to generate symmetric keys and encrypt process. This method has been tested based on computation time, histogram analysis and statistical analysis. The results of the test were obtained that the proposed method was resistant to multiple attacks despite having slower computing time. The proposed compute time error percentage is 1.69% for key generator stages and 0.07% for total compute time at the encrypt-decrypt stage

A review and open issues of multifarious image steganography techniques in spatial domain

Nowadays, information hiding is becoming a helpful technique and fetch more attention due fast growth of using internet, it is applied for sending secret information by using different techniques. Steganography is one of major important technique in information hiding. Steganography is science of concealing the secure information within a carrier object to provide the secure communication though the internet, so that no one can recognize and detect it’s except the sender & receiver. In steganography, many various carrier formats can be used such as an image, video, protocol, audio. The digital image is most popular used as a carrier file due its frequency on internet. There are many techniques variable for image steganography, each has own strong and weak points. In this study, we conducted a review of image steganography in spatial domain to explore the term image steganography by reviewing, collecting, synthesizing and analyze the challenges of different studies which related to this area published from 2014 to 2017. The aims of this review is provides an overview of image steganography and comparison between approved studies are discussed according to the pixel selection, payload capacity and embedding algorithm to open important research issues in the future works and obtain a robust method

Symmetric and Asymmetric Based Encryption Model for Mobile Commerce

Secure information exchange in a mobile commerce environment has become a difficult task due to the involvement of sensitive financial information and the tremendous development in information technology in recent years. This poses a great threat while conducting transaction in a mobile commerce environment. Cryptography has been employed to eliminate this using symmetric and asymmetric cryptography. However, in symmetric cryptography, secret key distribution can create a performance bottleneck, while asymmetric ciphers consume significant computational resources. This paper proposed symmetric and asymmetric based encryption model so as to achieve robust security and faster processing speed, by employing Advanced Encryption Standard (AES) as symmetric algorithm and Rivest-Shamir-Adleman (RSA) as asymmetric algorithm. In this model, RSA was used to encrypt AES secret key in order to secure the exchange of the key, while the rest of the sensitive data was encrypted using AES. The proposed model was implemented using Java programming language. Performance evaluation of the proposed model was carried out in terms of encryption/decryption time and the results show that the proposed model takes a little longer time than the RSA algorithm. This is as a result of the AES key encryption being introduced into the model. It is therefore recommended that the model be implemented in mobile commerce applications as an added layer of security in order to strengthen the applications against numerous security threats due to the robust security and faster processing speed provided by the proposed model

AN EFFICIENT CHAOS-BASED OPTIMIZATION ALGORITHM APPROACH FOR CRYPTOGRAPHY

The utmost negative impact of advancement of technology is an exponential increase in security threats, due to which tremendous demand for effective electronic security is increasing importantly. The principles of any security mechanism are confidentiality, authentication, integrity, non-repudiation, access control and availability. Cryptography is an essential aspect for secure communications. Many chaotic cryptosystem has been developed, as a result of the interesting relationship between the two field chaos and cryptography phenomenological behavior. In this paper, an overview of cryptography, optimization algorithm and chaos theory is provided and a novel approach for encryption and decryption based on chaos and optimization algorithms is discussed. In this article, the basic idea is to encrypt and decrypt the information using the concept of genetic algorithm with the pseudorandom sequence further used as a key in genetic algorithm operation for encryption: which is generated by application of chaotic map. This attempt result in good desirable cryptographic properties as a change in key will produce undesired result in receiver side. The suggested approach complements standard, algorithmic procedures, providing security solutions with novel features

A Naïve Visual Cryptographic Algorithm for the Transfer of Compressed Medical Images

The transmission of a suitably compressed image over a bandwidth, over long distances, gives rise towards a new era in the field of information technology. A gradual increase in this appending scenic application, involving the transfer of the images securely over the Ethernet has become an increasingly important aspect to be addressed during thou phenomenon, especially in the transfer of the digital medical images vividly, encapsulated with abundant information related to these images. The compressed medical images of the DICOM format contain certain amount of confidential data, pertaining to a clinical research or to an individual, and the confidentiality of the same has to be preserved from various security threats and eves-dropping. With a widespread applications among various multimedia applicative systems, telemedicine, medical imaging, military and certain safety-critical applications, inter-net and intra-net communicative applications, etc, a reliable transfer of suitable information, efficiently & securely is considered as one of the revolutionary aims in today’s communication technology and visual cryptographic methodologies. Real-time applications as such detailed above majorly is concerned with the security measures and many algorithms have been developed as a proof for various visual cryptographic methodologies. In this paper we propose an efficient and a reliable visual cryptographic methodology which focuses on the encryption and decryption of the two-dimensional DICOM standard compressed medical image, effectively.  This paper discusses an efficient design of 192 bit encoder using AES Rijndael Algorithm with the decomposition of an image into square image size blocks and the image blocks are shuffled using 2D CAT map. The shuffling of the image blocks/pixels employs a Logistic map of these image pixels coupled with 2D mapping of the pixels of the DICOM standard medical image, generated randomly, being the control parameter thereby creating a confusion between the cipher and the plain image, gradually increasing the resistive factor against the significant attacks. This paper proposes various analytical metrics such as correlation analysis, entropy analysis, homogeneity analysis, energy analysis, contrast and mean of absolute deviation analysis, to evaluate the proposed algorithm, and their suitability in image encryption applications

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

A reversible system based on hybrid toggle radius-4 cellular automata and its application as a block cipher

The dynamical system described herein uses a hybrid cellular automata (CA) mechanism to attain reversibility, and this approach is adapted to create a novel block cipher algorithm called HCA. CA are widely used for modeling complex systems and employ an inherently parallel model. Therefore, applications derived from CA have a tendency to fit very well in the current computational paradigm where scalability and multi-threading potential are quite desirable characteristics. HCA model has recently received a patent by the Brazilian agency INPI. Several evaluations and analyses performed on the model are presented here, such as theoretical discussions related to its reversibility and an analysis based on graph theory, which reduces HCA security to the well-known Hamiltonian cycle problem that belongs to the NP-complete class. Finally, the cryptographic robustness of HCA is empirically evaluated through several tests, including avalanche property compliance and the NIST randomness suite.Comment: 34 pages, 12 figure

An analysis and a comparative study of cryptographic algorithms used on the internet of things (IoT) based on avalanche effect

Ubiquitous computing is already weaving itself around us and it is connecting everything to the network of networks. This interconnection of objects to the internet is new computing paradigm called the Internet of Things (IoT) networks. Many capacity and non-capacity constrained devices, such as sensors are connecting to the Internet. These devices interact with each other through the network and provide a new experience to its users. In order to make full use of this ubiquitous paradigm, security on IoT is important. There are problems with privacy concerns regarding certain algorithms that are on IoT, particularly in the area that relates to their avalanche effect means that a small change in the plaintext or key should create a significant change in the ciphertext. The higher the significant change, the higher the security if that algorithm. If the avalanche effect of an algorithm is less than 50% then that algorithm is weak and can create security undesirability in any network. In this, case IoT. In this study, we propose to do the following: (1) Search and select existing block cryptographic algorithms (maximum of ten) used for authentication and encryption from different devices used on IoT. (2) Analyse the avalanche effect of select cryptographic algorithms and determine if they give efficient authentication on IoT. (3) Improve their avalanche effect by designing a mathematical model that improves their robustness against attacks. This is done through the usage of the initial vector XORed with plaintext and final vector XORed with cipher tect. (4) Test the new mathematical model for any enhancement on the avalanche effect of each algorithm as stated in the preceding sentences. (5) Propose future work on how to enhance security on IoT. Results show that when using the proposed method with variation of key, the avalanche effect significantly improved for seven out of ten algorithms. This means that we have managed to improve 70% of algorithms tested. Therefore indicating a substantial success rate for the proposed method as far as the avalanche effect is concerned. We propose that the seven algorithms be replaced by our improved versions in each of their implementation on IoT whenever the plaintext is varied.Electrical and Mining EngineeringM. Tech. (Electrical Engineering

Trusted SoC Realization for Remote Dynamic IP Integration

Heutzutage bieten field-programmable gate arrays (FPGAs) enorme Rechenleistung und Flexibilität. Zudem sind sie oft auf einem einzigen Chip mit eingebetteten Multicore-Prozessoren, DSP-Engines und Speicher-Controllern integriert. Dadurch sind sie für große und komplexe Anwendungen geeignet. Gleichzeitig führten die Fortschritte auf dem Gebiet der High-Level-Synthese und die Verfügbarkeit standardisierter Schnittstellen (wie etwa das Advanced eXtensible Interface 4) zur Entwicklung spezialisierter und neuartiger Funktionalitäten durch Designhäuser. All dies schuf einen Bedarf für ein Outsourcing der Entwicklung oder die Lizenzierung von FPGA-IPs (Intellectual Property). Ein Pay-per-Use IP-Lizenzierungsmodell, bei dem diese IPs vor allen Marktteilnehmern geschützt sind, kommt den Entwicklern der IPs zugute. Außerdem handelt es sich bei den Entwicklern von FPGA-Systemen in der Regel um kleine bis mittlere Unternehmen, die in Bezug auf die Markteinführungszeit und die Kosten pro Einheit von einem solchen Lizenzierungsmodell profitieren können. Im akademischen Bereich und in der Industrie gibt es mehrere IP-Lizenzierungsmodelle und Schutzlösungen, die eingesetzt werden können, die jedoch mit zahlreichen Sicherheitsproblemen behaftet sind. In einigen Fällen verursachen die vorgeschlagenen Sicherheitsmaßnahmen einen unnötigen Ressourcenaufwand und Einschränkungen für die Systementwickler, d. h., sie können wesentliche Funktionen ihres Geräts nicht nutzen. Darüber hinaus lassen sie zwei funktionale Herausforderungen außer Acht: das Floorplanning der IP auf der programmierbaren Logik (PL) und die Generierung des Endprodukts der IP (Bitstream) unabhängig vom Gesamtdesign. In dieser Arbeit wird ein Pay-per-Use-Lizenzierungsschema vorgeschlagen und unter Verwendung eines security framework (SFW) realisiert, um all diese Herausforderungen anzugehen. Das vorgestellte Schema ist pragmatisch, weniger restriktiv für Systementwickler und bietet Sicherheit gegen IP-Diebstahl. Darüber hinaus werden Maßnahmen ergriffen, um das System vor einem IP zu schützen, das bösartige Schaltkreise enthält. Das „Secure Framework“ umfasst ein vertrauenswürdiges Betriebssystem, ein reichhaltiges Betriebssystem, mehrere unterstützende Komponenten (z. B. TrustZone- Logik, gegen Seitenkanalangriffe (SCA) resistente Entschlüsselungsschaltungen) und Softwarekomponenten, z. B. für die Bitstromanalyse. Ein Gerät, auf dem das SFW läuft, kann als vertrauenswürdiges Gerät betrachtet werden, das direkt mit einem Repository oder einem IP-Core-Entwickler kommunizieren kann, um IPs in verschlüsselter Form zu erwerben. Die Entschlüsselung und Authentifizierung des IPs erfolgt auf dem Gerät, was die Angriffsfläche verringert und es weniger anfällig für IP-Diebstahl macht. Außerdem werden Klartext-IPs in einem geschützten Speicher des vertrauenswürdigen Betriebssystems abgelegt. Das Klartext-IP wird dann analysiert und nur dann auf der programmierbaren Logik konfiguriert, wenn es authentisch ist und keine bösartigen Schaltungen enthält. Die Bitstrom-Analysefunktionalität und die SFW-Unterkomponenten ermöglichen die Partitionierung der PL-Ressourcen in sichere und unsichere Ressourcen, d. h. die Erweiterung desKonzepts der vertrauenswürdigen Ausführungsumgebung (TEE) auf die PL. Dies ist die erste Arbeit, die das TEE-Konzept auf die programmierbare Logik ausweitet. Bei der oben erwähnten SCA-resistenten Entschlüsselungsschaltung handelt es sich um die Implementierung des Advanced Encryption Standard, der so modifiziert wurde, dass er gegen elektromagnetische und stromverbrauchsbedingte Leckagen resistent ist. Das geschützte Design verfügt über zwei Gegenmaßnahmen, wobei die erste auf einer Vielzahl unterschiedler Implementierungsvarianten und veränderlichen Zielpositionen bei der Konfiguration basiert, während die zweite nur unterschiedliche Implementierungsvarianten verwendet. Diese Gegenmaßnahmen sind auch während der Laufzeit skalierbar. Bei der Bewertung werden auch die Auswirkungen der Skalierbarkeit auf den Flächenbedarf und die Sicherheitsstärke berücksichtigt. Darüber hinaus wird die zuvor erwähnte funktionale Herausforderung des IP Floorplanning durch den Vorschlag eines feinkörnigen Automatic Floorplanners angegangen, der auf gemischt-ganzzahliger linearer Programmierung basiert und aktuelle FPGAGenerationen mit größeren und komplexen Bausteine unterstützt. Der Floorplanner bildet eine Reihe von IPs auf dem FPGA ab, indem er präzise rekonfigurierbare Regionen schafft. Dadurch werden die verbleibenden verfügbaren Ressourcen für das Gesamtdesign maximiert. Die zweite funktionale Herausforderung besteht darin, dass die vorhandenen Tools keine native Funktionalität zur Erzeugung von IPs in einer eigenständigen Umgebung bieten. Diese Herausforderung wird durch den Vorschlag eines unabhängigen IP-Generierungsansatzes angegangen. Dieser Ansatz kann von den Marktteilnehmern verwendet werden, um IPs eines Entwurfs unabhängig vom Gesamtentwurf zu generieren, ohne die Kompatibilität der IPs mit dem Gesamtentwurf zu beeinträchtigen

QUALITY-DRIVEN CROSS LAYER DESIGN FOR MULTIMEDIA SECURITY OVER RESOURCE CONSTRAINED WIRELESS SENSOR NETWORKS

The strong need for security guarantee, e.g., integrity and authenticity, as well as privacy and confidentiality in wireless multimedia services has driven the development of an emerging research area in low cost Wireless Multimedia Sensor Networks (WMSNs). Unfortunately, those conventional encryption and authentication techniques cannot be applied directly to WMSNs due to inborn challenges such as extremely limited energy, computing and bandwidth resources. This dissertation provides a quality-driven security design and resource allocation framework for WMSNs. The contribution of this dissertation bridges the inter-disciplinary research gap between high layer multimedia signal processing and low layer computer networking. It formulates the generic problem of quality-driven multimedia resource allocation in WMSNs and proposes a cross layer solution. The fundamental methodologies of multimedia selective encryption and stream authentication, and their application to digital image or video compression standards are presented. New multimedia selective encryption and stream authentication schemes are proposed at application layer, which significantly reduces encryption/authentication complexity. In addition, network resource allocation methodologies at low layers are extensively studied. An unequal error protection-based network resource allocation scheme is proposed to achieve the best effort media quality with integrity and energy efficiency guarantee. Performance evaluation results show that this cross layer framework achieves considerable energy-quality-security gain by jointly designing multimedia selective encryption/multimedia stream authentication and communication resource allocation