Systematizing Genome Privacy Research: A Privacy-Enhancing Technologies Perspective

Rapid advances in human genomics are enabling researchers to gain a better understanding of the role of the genome in our health and well-being, stimulating hope for more effective and cost efficient healthcare. However, this also prompts a number of security and privacy concerns stemming from the distinctive characteristics of genomic data. To address them, a new research community has emerged and produced a large number of publications and initiatives. In this paper, we rely on a structured methodology to contextualize and provide a critical analysis of the current knowledge on privacy-enhancing technologies used for testing, storing, and sharing genomic data, using a representative sample of the work published in the past decade. We identify and discuss limitations, technical challenges, and issues faced by the community, focusing in particular on those that are inherently tied to the nature of the problem and are harder for the community alone to address. Finally, we report on the importance and difficulty of the identified challenges based on an online survey of genome data privacy expertsComment: To appear in the Proceedings on Privacy Enhancing Technologies (PoPETs), Vol. 2019, Issue

Crowdfunding Non-fungible Tokens on the Blockchain

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

Fake Malware Generation Using HMM and GAN

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

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

Security and Privacy for Modern Wireless Communication Systems

The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

Revealing the Landscape of Privacy-Enhancing Technologies in the Context of Data Markets for the IoT: A Systematic Literature Review

IoT data markets in public and private institutions have become increasingly relevant in recent years because of their potential to improve data availability and unlock new business models. However, exchanging data in markets bears considerable challenges related to disclosing sensitive information. Despite considerable research focused on different aspects of privacy-enhancing data markets for the IoT, none of the solutions proposed so far seems to find a practical adoption. Thus, this study aims to organize the state-of-the-art solutions, analyze and scope the technologies that have been suggested in this context, and structure the remaining challenges to determine areas where future research is required. To accomplish this goal, we conducted a systematic literature review on privacy enhancement in data markets for the IoT, covering 50 publications dated up to July 2020, and provided updates with 24 publications dated up to May 2022. Our results indicate that most research in this area has emerged only recently, and no IoT data market architecture has established itself as canonical. Existing solutions frequently lack the required combination of anonymization and secure computation technologies. Furthermore, there is no consensus on the appropriate use of blockchain technology for IoT data markets and a low degree of leveraging existing libraries or reusing generic data market architectures. We also identified significant challenges remaining, such as the copy problem and the recursive enforcement problem that-while solutions have been suggested to some extent-are often not sufficiently addressed in proposed designs. We conclude that privacy-enhancing technologies need further improvements to positively impact data markets so that, ultimately, the value of data is preserved through data scarcity and users' privacy and businesses-critical information are protected.Comment: 49 pages, 17 figures, 11 table

Cloud technology options towards Free Flow of Data

This whitepaper collects the technology solutions that the projects in the Data Protection, Security and Privacy Cluster propose to address the challenges raised by the working areas of the Free Flow of Data initiative. The document describes the technologies, methodologies, models, and tools researched and developed by the clustered projects mapped to the ten areas of work of the Free Flow of Data initiative. The aim is to facilitate the identification of the state-of-the-art of technology options towards solving the data security and privacy challenges posed by the Free Flow of Data initiative in Europe. The document gives reference to the Cluster, the individual projects and the technologies produced by them

Internet of Things Strategic Research Roadmap

Internet of Things (IoT) is an integrated part of Future Internet including existing and evolving Internet and network developments and could be conceptually defined as a dynamic global network infrastructure with self configuring capabilities based on standard and interoperable communication protocols where physical and virtual “things” have identities, physical attributes, and virtual personalities, use intelligent interfaces, and are seamlessly integrated into the information network