Deep neural networks in the cloud: Review, applications, challenges and research directions

Deep neural networks (DNNs) are currently being deployed as machine learning technology in a wide range of important real-world applications. DNNs consist of a huge number of parameters that require millions of floating-point operations (FLOPs) to be executed both in learning and prediction modes. A more effective method is to implement DNNs in a cloud computing system equipped with centralized servers and data storage sub-systems with high-speed and high-performance computing capabilities. This paper presents an up-to-date survey on current state-of-the-art deployed DNNs for cloud computing. Various DNN complexities associated with different architectures are presented and discussed alongside the necessities of using cloud computing. We also present an extensive overview of different cloud computing platforms for the deployment of DNNs and discuss them in detail. Moreover, DNN applications already deployed in cloud computing systems are reviewed to demonstrate the advantages of using cloud computing for DNNs. The paper emphasizes the challenges of deploying DNNs in cloud computing systems and provides guidance on enhancing current and new deployments.The EGIA project (KK-2022/00119The Consolidated Research Group MATHMODE (IT1456-22

Scalable and Secure Big Data IoT System Based on Multifactor Authentication and Lightweight Cryptography

© 2013 IEEE. Organizations share an evolving interest in adopting a cloud computing approach for Internet of Things (IoT) applications. Integrating IoT devices and cloud computing technology is considered as an effective approach to storing and managing the enormous amount of data generated by various devices. However, big data security of these organizations presents a challenge in the IoT-cloud architecture. To overcome security issues, we propose a cloud-enabled IoT environment supported by multifactor authentication and lightweight cryptography encryption schemes to protect big data system. The proposed hybrid cloud environment is aimed at protecting organizations\u27 data in a highly secure manner. The hybrid cloud environment is a combination of private and public cloud. Our IoT devices are divided into sensitive and nonsensitive devices. Sensitive devices generate sensitive data, such as healthcare data; whereas nonsensitive devices generate nonsensitive data, such as home appliance data. IoT devices send their data to the cloud via a gateway device. Herein, sensitive data are split into two parts: one part of the data is encrypted using RC6, and the other part is encrypted using the Fiestel encryption scheme. Nonsensitive data are encrypted using the Advanced Encryption Standard (AES) encryption scheme. Sensitive and nonsensitive data are respectively stored in private and public cloud to ensure high security. The use of multifactor authentication to access the data stored in the cloud is also proposed. During login, data users send their registered credentials to the Trusted Authority (TA). The TA provides three levels of authentication to access the stored data: first-level authentication - read file, second-level authentication - download file, and third-level authentication - download file from the hybrid cloud. We implement the proposed cloud-IoT architecture in the NS3 network simulator. We evaluated the performance of the proposed architecture using metrics such as computational time, security strength, encryption time, and decryption time

Protection of data privacy based on artificial intelligence in Cyber-Physical Systems

With the rapid evolution of cyber attack techniques, the security and privacy of Cyber-Physical Systems (CPSs) have become key challenges. CPS environments have several properties that make them unique in efforts to appropriately secure them when compared with the processes, techniques and processes that have evolved for traditional IT networks and platforms. CPS ecosystems are comprised of heterogeneous systems, each with long lifespans. They use multitudes of operating systems and communication protocols and are often designed without security as a consideration. From a privacy perspective, there are also additional challenges. It is hard to capture and filter the heterogeneous data sources of CPSs, especially power systems, as their data should include network traffic and the sensing data of sensors. Protecting such data during the stages of collection, analysis and publication still open the possibility of new cyber threats disrupting the operational loops of power systems. Moreover, while protecting the original data of CPSs, identifying cyberattacks requires intrusion detection that produces high false alarm rates. This thesis significantly contributes to the protection of heterogeneous data sources, along with the high performance of discovering cyber-attacks in CPSs, especially smart power networks (i.e., power systems and their networks). For achieving high data privacy, innovative privacy-preserving techniques based on Artificial Intelligence (AI) are proposed to protect the original and sensitive data generated by CPSs and their networks. For cyber-attack discovery, meanwhile applying privacy-preserving techniques, new anomaly detection algorithms are developed to ensure high performances in terms of data utility and accuracy detection. The first main contribution of this dissertation is the development of a privacy preservation intrusion detection methodology that uses the correlation coefficient, independent component analysis, and Expectation Maximisation (EM) clustering algorithms to select significant data portions and discover cyber attacks against power networks. Before and after applying this technique, machine learning algorithms are used to assess their capabilities to classify normal and suspicious vectors. The second core contribution of this work is the design of a new privacy-preserving anomaly detection technique protecting the confidential information of CPSs and discovering malicious observations. Firstly, a data pre-processing technique filters and transforms data into a new format that accomplishes the aim of preserving privacy. Secondly, an anomaly detection technique using a Gaussian mixture model which fits selected features, and a Kalman filter technique that accurately computes the posterior probabilities of legitimate and anomalous events are employed. The third significant contribution of this thesis is developing a novel privacy-preserving framework for achieving the privacy and security criteria of smart power networks. In the first module, a two-level privacy module is developed, including an enhanced proof of work technique-based blockchain for accomplishing data integrity and a variational autoencoder approach for changing the data to an encoded data format to prevent inference attacks. In the second module, a long short-term memory deep learning algorithm is employed in anomaly detection to train and validate the outputs from the two-level privacy modules

Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses

The ongoing deployment of the fifth generation (5G) wireless networks constantly reveals limitations concerning its original concept as a key driver of Internet of Everything (IoE) applications. These 5G challenges are behind worldwide efforts to enable future networks, such as sixth generation (6G) networks, to efficiently support sophisticated applications ranging from autonomous driving capabilities to the Metaverse. Edge learning is a new and powerful approach to training models across distributed clients while protecting the privacy of their data. This approach is expected to be embedded within future network infrastructures, including 6G, to solve challenging problems such as resource management and behavior prediction. This survey article provides a holistic review of the most recent research focused on edge learning vulnerabilities and defenses for 6G-enabled IoT. We summarize the existing surveys on machine learning for 6G IoT security and machine learning-associated threats in three different learning modes: centralized, federated, and distributed. Then, we provide an overview of enabling emerging technologies for 6G IoT intelligence. Moreover, we provide a holistic survey of existing research on attacks against machine learning and classify threat models into eight categories, including backdoor attacks, adversarial examples, combined attacks, poisoning attacks, Sybil attacks, byzantine attacks, inference attacks, and dropping attacks. In addition, we provide a comprehensive and detailed taxonomy and a side-by-side comparison of the state-of-the-art defense methods against edge learning vulnerabilities. Finally, as new attacks and defense technologies are realized, new research and future overall prospects for 6G-enabled IoT are discussed

Preserving Secrecy in Online Social Networks: Data Outsourcing, Access Control, and Secrecy Schemes

In den vergangenen Jahren haben sich Online Social Networks (OSNs) wie Facebook und Foursquare zu einer beliebten Möglichkeit der Kommunikation und des Teilens von Informationen unter Nutzern entwickelt. OSNs sind virtuelle Communitys, die Informationen über die Nutzer und die zwischen ihnen bestehenden Beziehungen, wie z.~B. Freundschaften, enthalten. Zusätzlich dazu, dass eine Interaktion der Nutzer untereinander ermöglicht wird, bieten OSNs ihren Nutzern normalerweise verschiedene Arten von Dienstleistungen an, wie z.~B. die Abfrage nach Freunden innerhalb einer bestimmten Entfernung. Um auf diese Dienstleistungen zugreifen zu können, kann es sein, dass Nutzer darum gebeten werden, in den OSN-Systemen eine Reihe von Informationen, wie z.~B. ihre physische Position, zu speichern. Da die meisten der in OSNs gespeicherten Informationen zu deren Nutzern privater Natur sind, ist es von wesentlicher Bedeutung, die Informationen vor unbefugtem Zugriff zu schützen, um Geheimhaltungsprobleme zu vermeiden. Zu diesem Zweck verwenden OSNs Zugriffskontrollsysteme. Diese Systeme haben drei Hauptkomponenten, nämlich die Zugriffskontrollrichtlinien, das Zugriffskontrollmodell und den Autorisierungsmechanismus. Die Zugriffskontrollrichtlinien ermöglichen es Nutzern zu spezifizieren, wer auf deren Ressourcen zugreifen darf. Das Zugriffskontrollmodell bietet die Syntax und Semantik, um die Zugriffskontrollrichtlinien zu formalisieren. Die formale Repräsentation der Zugriffskontrollrichtlinien in einem Zugriffskontrollmodell wird als Autorisierung bezeichnet. Der Autorisierungsmechanismus, welcher von den OSN-Anbietern verwaltet wird, setzt die Autorisierungen durch. Obwohl in der Literatur verschiedene Zugriffskontrollsysteme vorgeschlagen wurden, gibt es zwei Hauptprobleme in Bezug auf diese Systeme, die sich auf die Verbreitung von OSNs auswirken können. Das erste Problem bezieht sich auf die Flexibilität von Zugriffskontrollmodellen. Eine der größten Herausforderungen von OSNs besteht darin, das Teilen von Informationen unter ihren Nutzern zu fördern. Nutzer neigen normalerweise dazu, Informationen nur mit Nutzern zu teilen, die bestimmte Bedingungen erfüllen; andernfalls tun sie es nicht. Zu diesem Zweck sollten Zugriffskontrollsysteme den Spezifizierern der Richtlinien Flexibilität bieten, damit diese die Bedingungen bezüglich des Zugriffs auf ihre Daten ausdrücken können. Wenn Nutzer entscheiden, wer auf ihre Ressourcen zugreifen darf, hängen die Zugriffsbedingungen von sozialen Faktoren und menschlichem Verhalten ab. Studien in Fachgebieten wie der Psychologie und der Soziologie haben nachgewiesen, dass Menschen zwar ein Selbstinteresse haben, oftmals jedoch gegenseitig von dieser Haltung abweichen. Gegenseitigkeit bedeutet, dass Menschen als Antwort auf freundliche Handlungen kooperativer werden. Daher ist Gegenseitigkeit eine starke Determinante in Bezug auf menschliches Verhalten. Bestehende Zugriffsrichtlinien erfassen dieses Phänomen der Gegenseitigkeit jedoch nicht, was dazu führen kann, dass Nutzer davon abgehalten werden, Informationen zu teilen. Das zweite Problem besteht darin, dass Nutzer OSN-Anbietern dahingehend vertrauen müssen, dass sie ihre Daten schützen, wenn sie die Autorisierungen durchsetzen. Aktuelle Datenschutzverletzungen haben die Vertrauenswürdigkeit der Dienstleistungsanbieter in Frage gestellt. Scheinbar steigert der zunehmende wirtschaftliche Gewinn, der aus dem Verkauf personenbezogener Daten erzielt wird, die Versuchung der Anbieter, Betrug zu begehen. In dieser Dissertation werden Techniken und Modelle entwickelt, um auf diese zwei Probleme einzugehen. Die Arbeit ist in drei Abschnitte aufgeteilt. Der erster Beitrag behandelt das Flexibilitätsproblem von Zugriffskontrollmodellen. Hier schlagen wir die Syntax und Semantik einer neuen Art von Autorisierung vor, die als gegenseitig bezeichnet wird und es ermöglicht, wechselseitiges Verhalten zu modellieren. Gegenseitigkeit kommt im Rahmen der Zugriffskontrolle zum Zuge, wenn Personen jenen Nutzern den Zugriff auf ihre Ressourcen gewähren, die ihnen erlauben, das Gleiche zu tun. Wir verwenden standortbasierte Dienstleistungen als Beispiel für den Einsatz gegenseitiger Autorisierungen. Zu diesem Zweck schlagen wir zwei Ansätze vor, um gegenseitige Autorisierungen in diese Dienstleistungen zu integrieren. Darüber hinaus weisen wir die Stimmigkeit beider Ansätze nach und bestimmen auf dem Wege von Komplexitätsanalysen, unter welchen Bedingungen jeder Ansatz jeweils leistungsfähiger ist als der andere. Unsere zweiten und dritten Beiträge gehen aus zwei verschiedenen Blickwinkeln auf das Misstrauen von Nutzern bezüglich der Dienstleistungsanbieter ein. Unser zweiter Beitrag erörtert das Szenario, in welchem der Nutzer, d. h. die Einheit, welche Abfragen von Daten durchführen möchte, auch Eigentümer der Daten ist. Aufgrund von Ressourcenbeschränkungen möchte der Nutzer die Daten jedoch nicht allein verwalten. Er möchte dies an einen Dienstleistungsanbieter auslagern, um bei einer Abfrage einen Teil der Daten abrufen zu können, welche der Durchführung der Abfrage Genüge leisten. In diesem Fall besteht kein Bedarf an Zugriffsrichtlinien, da es einen einzelnen Nutzer gibt, der Eigentümer der Daten ist. Daher kann in diesem Szenario das Vertrauensproblem bezüglich Dienstleistungsanbietern auf die Geheimhaltung ausgelagerter Daten reduziert werden. Außerdem ist es für den Nutzer wichtig, in der Lage zu sein, eine Anpassung zwischen Geheimhaltung und Leistung vorzunehmen, da die Abfrage nutzerseitig, unter Verwendung des erhaltenen Datenabschnitts, berechnet wird und weil eine negative Korrelation zwischen Geheimhaltung und Leistung besteht. Diese Art von Szenario findet aufgrund der wirtschaftlichen und organisatorischen Vorteile von „Database-as-a-Service“ oft bei Startup-Unternehmen Anwendung. Insbesondere in diesem Bereich weisen viele Daten eine Graphstruktur auf, z.~B. Protein-Netzwerke, Straßen-Netzwerke und Stromnetz-Netzwerke. Hier schlagen wir einen Gruppierungsansatz für die sichere Auslagerung von Daten mit Graphstrukturen vor, wobei nachweisbare Geheimhaltungsgarantien geboten werden. Unser Ansatz ermöglicht es Nutzern, Anpassungen zwischen Ebenen von Geheimhaltung und Leistung vorzunehmen. Zusätzlich entwickeln wir zur Erleichterung der Planung von Abfragen ein Modell, welches das Verhalten unseres Algorithmus vorhersagen kann. Unser dritter Beitrag berücksichtigt den Fall, in dem es einem Nutzer nicht ermöglicht wird, auf Daten zuzugreifen, die zur Durchführung von Abfragen nötig sind. Die Nutzer haben jedoch Zugriff auf die Ergebnisse der Abfrage bezüglich der Daten. In diesem Szenario gibt es typischerweise mehrere Nutzer, wobei jeder einen anderen Teil der Daten besitzt, und jeder Nutzer auf Basis von spezifizierten Zugriffsrichtlinien auf Abfrageergebnisse bezüglich der Daten zugreifen kann, die anderen gehören. Dann muss der OSN-Anbieter die erforderliche Kernberechnung durchführen, und der Nutzer kann nur auf das Ergebnis von Dienstleistungen zugreifen, die vom OSN geboten werden. Für dieses Szenario entwickeln wir zwei Methoden, welche bestehende Verschlüsselungsschemata kombinieren, um es Nutzern von OSNs zu ermöglichen, Abfragen bezüglich Freunden in einer bestimmten Entfernung durchzuführen. Beide Ansätze beinhalten eine Aufhebungsfunktion und bieten Geheimhaltungsgarantien unter der Annahme geheimer Absprachen, d. h. ein Gegenspieler kann mit dem Dienstleistungsanbieter zusammenspielen. Daneben bieten wir Komplexitätsanalysen unserer Ansätze, um diese bewerten und vergleichen zu können. Unsere Analysen teilen uns mit, welcher Ansatz in jeder Einheit, die in dem System involviert ist, leistungsfähiger ist. Diese Dissertation beinhaltet eine umfassende experimentelle Analyse all unserer Ansätze auf Basis von synthetischen und realen Datensätzen, welche die Wirksamkeit unserer Methoden bestätigen

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 Dynamic Four-Step Data Security Model for Data in Cloud Computing Based on Cryptography and Steganography

Cloud computing is a rapidly expanding field. It allows users to access computer system resources as needed, particularly data storage and computational power, without managing them directly. This paper aims to create a data security model based on cryptography and steganography for data in cloud computing that seeks to reduce existing security and privacy concerns, such as data loss, data manipulation, and data theft. To identify the problem and determine its core cause, we studied various literature on existing cloud computing security models. This study utilizes design science research methodology. The design science research approach includes problem identification, requirements elicitation, artifact design and development, demonstration, and assessment. Design thinking and the Python programming language are used to build the artifact, and discussion about its working is represented using histograms, tables, and algorithms. This paper’s output is a four-step data security model based on Rivest–Shamir–Adleman, Advanced Encryption Standard, and identity-based encryption algorithms alongside Least Significant Bit steganography. The four steps are data protection and security through encryption algorithms, steganography, data backup and recovery, and data sharing. This proposed approach ensures more cloud data redundancy, flexibility, efficiency, and security by protecting data confidentiality, privacy, and integrity from attackers

Blockchain for secured IoT and D2D applications over 5G cellular networks : a thesis by publications presented in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer and Electronics Engineering, Massey University, Albany, New Zealand

Author's Declaration: "In accordance with Sensors, SpringerOpen, and IEEE’s copyright policy, this thesis contains the accepted and published version of each manuscript as the final version. Consequently, the content is identical to the published versions."The Internet of things (IoT) is in continuous development with ever-growing popularity. It brings significant benefits through enabling humans and the physical world to interact using various technologies from small sensors to cloud computing. IoT devices and networks are appealing targets of various cyber attacks and can be hampered by malicious intervening attackers if the IoT is not appropriately protected. However, IoT security and privacy remain a major challenge due to characteristics of the IoT, such as heterogeneity, scalability, nature of the data, and operation in open environments. Moreover, many existing cloud-based solutions for IoT security rely on central remote servers over vulnerable Internet connections. The decentralized and distributed nature of blockchain technology has attracted significant attention as a suitable solution to tackle the security and privacy concerns of the IoT and device-to-device (D2D) communication. This thesis explores the possible adoption of blockchain technology to address the security and privacy challenges of the IoT under the 5G cellular system. This thesis makes four novel contributions. First, a Multi-layer Blockchain Security (MBS) model is proposed to protect IoT networks while simplifying the implementation of blockchain technology. The concept of clustering is utilized to facilitate multi-layer architecture deployment and increase scalability. The K-unknown clusters are formed within the IoT network by applying a hybrid Evolutionary Computation Algorithm using Simulated Annealing (SA) and Genetic Algorithms (GA) to structure the overlay nodes. The open-source Hyperledger Fabric (HLF) Blockchain platform is deployed for the proposed model development. Base stations adopt a global blockchain approach to communicate with each other securely. The quantitative arguments demonstrate that the proposed clustering algorithm performs well when compared to the earlier reported methods. The proposed lightweight blockchain model is also better suited to balance network latency and throughput compared to a traditional global blockchain. Next, a model is proposed to integrate IoT systems and blockchain by implementing the permissioned blockchain Hyperledger Fabric. The security of the edge computing devices is provided by employing a local authentication process. A lightweight mutual authentication and authorization solution is proposed to ensure the security of tiny IoT devices within the ecosystem. In addition, the proposed model provides traceability for the data generated by the IoT devices. The performance of the proposed model is validated with practical implementation by measuring performance metrics such as transaction throughput and latency, resource consumption, and network use. The results indicate that the proposed platform with the HLF implementation is promising for the security of resource-constrained IoT devices and is scalable for deployment in various IoT scenarios. Despite the increasing development of blockchain platforms, there is still no comprehensive method for adopting blockchain technology on IoT systems due to the blockchain's limited capability to process substantial transaction requests from a massive number of IoT devices. The Fabric comprises various components such as smart contracts, peers, endorsers, validators, committers, and Orderers. A comprehensive empirical model is proposed that measures HLF's performance and identifies potential performance bottlenecks to better meet blockchain-based IoT applications' requirements. The implementation of HLF on distributed large-scale IoT systems is proposed. The performance of the HLF is evaluated in terms of throughput, latency, network sizes, scalability, and the number of peers serviceable by the platform. The experimental results demonstrate that the proposed framework can provide a detailed and real-time performance evaluation of blockchain systems for large-scale IoT applications. The diversity and the sheer increase in the number of connected IoT devices have brought significant concerns about storing and protecting the large IoT data volume. Dependencies of the centralized server solution impose significant trust issues and make it vulnerable to security risks. A layer-based distributed data storage design and implementation of a blockchain-enabled large-scale IoT system is proposed to mitigate these challenges by using the HLF platform for distributed ledger solutions. The need for a centralized server and third-party auditor is eliminated by leveraging HLF peers who perform transaction verification and records audits in a big data system with the help of blockchain technology. The HLF blockchain facilitates storing the lightweight verification tags on the blockchain ledger. In contrast, the actual metadata is stored in the off-chain big data system to reduce the communication overheads and enhance data integrity. Finally, experiments are conducted to evaluate the performance of the proposed scheme in terms of throughput, latency, communication, and computation costs. The results indicate the feasibility of the proposed solution to retrieve and store the provenance of large-scale IoT data within the big data ecosystem using the HLF blockchain