Retrieving Encrypted Images Using Convolution Neural Network and Fully Homomorphic Encryption

استرجاع الصور المستند إلى المحتوى (CBIR) هو تقنية تستخدم لاسترداد الصور من قاعدة بيانات الصور. ومع ذلك، فإن عملية CBIR تعاني من دقة أقل في استرداد الصور من قاعدة بيانات صور واسعة النطاق وضمان خصوصية الصور. تهدف هذه الورقة إلى معالجة قضايا الدقة باستخدام تقنيات التعلم العميق كطريقة CNN. أيضًا، توفير الخصوصية اللازمة للصور باستخدام طرق تشفير متماثلة تمامًا بواسطة Cheon و Kim و Kim و Song (CKKS). ولتحقيق هذه الأهداف تم اقتراح نظام RCNN_CKKS يتضمن جزأين. يستخرج الجزء الأول (المعالجة دون اتصال بالإنترنت–) لاستخراج الخصائص العالية المستوى استنادًا إلى طبقة التسطيح في شبكة عصبية تلافيفية (CNN) ثم يخزن هذه الميزات في مجموعة بيانات جديدة. في الجزء الثاني (المعالجة عبر الإنترنت) ، يرسل العميل الصورة المشفرة إلى الخادم ، والتي تعتمد على نموذج CNN المدرب لاستخراج ميزات الصورة المرسلة. بعد ذلك، تتم مقارنة الميزات المستخرجة مع الميزات المخزنة باستخدام طريقة Hamming Distance لاسترداد جميع الصور المتشابهة. أخيرًا، يقوم الخادم بتشفير جميع الصور المسترجعة وإرسالها إلى العميل. كانت نتائج التعلم العميق على الصور العادية 97.94٪ للتصنيف و98.94٪ للصور المسترجعة. في الوقت نفسه، تم استخدام اختبار NIST للتحقق من أمان CKKS عند تطبيقه على مجموعة بيانات المعهد الكندي للأبحاث المتقدمة (CIFAR-10). من خلال هذه النتائج، استنتج الباحثون أن التعلم العميق هو وسيلة فعالة لاستعادة الصور وأن طريقة CKKS مناسبة لحماية خصوصية الصورة.A content-based image retrieval (CBIR) is a technique used to retrieve images from an image database. However, the CBIR process suffers from less accuracy to retrieve images from an extensive image database and ensure the privacy of images. This paper aims to address the issues of accuracy utilizing deep learning techniques as the CNN method. Also, it provides the necessary privacy for images using fully homomorphic encryption methods by Cheon, Kim, Kim, and Song (CKKS). To achieve these aims, a system has been proposed, namely RCNN_CKKS, that includes two parts. The first part (offline processing) extracts automated high-level features based on a flatting layer in a convolutional neural network (CNN) and then stores these features in a new dataset. In the second part (online processing), the client sends the encrypted image to the server, which depends on the CNN model trained to extract features of the sent image. Next, the extracted features are compared with the stored features using a Hamming distance method to retrieve all similar images. Finally, the server encrypts all retrieved images and sends them to the client. Deep-learning results on plain images were 97.94% for classification and 98.94% for retriever images. At the same time, the NIST test was used to check the security of CKKS when applied to Canadian Institute for Advanced Research (CIFAR-10) dataset. Through these results, researchers conclude that deep learning is an effective method for image retrieval and that a CKKS method is appropriate for image privacy protection

Theory and Practice of Cryptography and Network Security Protocols and Technologies

In an age of explosive worldwide growth of electronic data storage and communications, effective protection of information has become a critical requirement. When used in coordination with other tools for ensuring information security, cryptography in all of its applications, including data confidentiality, data integrity, and user authentication, is a most powerful tool for protecting information. This book presents a collection of research work in the field of cryptography. It discusses some of the critical challenges that are being faced by the current computing world and also describes some mechanisms to defend against these challenges. It is a valuable source of knowledge for researchers, engineers, graduate and doctoral students working in the field of cryptography. It will also be useful for faculty members of graduate schools and universities

정보 보호 기계 학습의 암호학 기반 기술: 근사 동형 암호와 부호 기반 암호

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2021. 2. 노종선.In this dissertation, three main contributions are given as; i) a protocol of privacy-preserving machine learning using network resources, ii) the development of approximate homomorphic encryption that achieves less error and high-precision bootstrapping algorithm without compromising performance and security, iii) the cryptanalysis and the modification of code-based cryptosystems: cryptanalysis on IKKR cryptosystem and modification of the pqsigRM, a digital signature scheme proposed to the post-quantum cryptography (PQC) standardization of National Institute of Standards and Technology (NIST). The recent development of machine learning, cloud computing, and blockchain raises a new privacy problem; how can one outsource computation on confidential data? Moreover, as research on quantum computers shows success, the need for PQC is also emerging. Multi-party computation (MPC) is the cryptographic protocol that makes computation on data without revealing it. Since MPC is designed based on homomorphic encryption (HE) and PQC, research on designing efficient and safe HE and PQC is actively being conducted. First, I propose a protocol for privacy-preserving machine learning (PPML) that replaces bootstrapping of homomorphic encryption with network resources. In general, the HE ciphertext has a limited depth of circuit that can be calculated, called the level of a ciphertext. We call bootstrapping restoring the level of ciphertext that has exhausted its level through a method such as homomorphic decryption. Bootstrapping of homomorphic encryption is, in general, very expensive in time and space. However, when deep computations like deep learning are performed, it is required to do bootstrapping. In this protocol, both the client's message and servers' intermediate values are kept secure, while the client's computation and communication complexity are light. Second, I propose an improved bootstrapping algorithm for the CKKS scheme and a method to reduce the error by homomorphic operations in the CKKS scheme. The Cheon-Kim-Kim-Song (CKKS) scheme (Asiacrypt '17) is one of the highlighted fully homomorphic encryption (FHE) schemes as it is efficient to deal with encrypted real numbers, which are the usual data type for many applications such as machine learning. However, the precision drop due to the error growth is a drawback of the CKKS scheme for data processing. I propose a method to achieve high-precision approximate FHE using the following two methods .First, I apply the signal-to-noise ratio (SNR) concept and propose methods to maximize SNR by reordering homomorphic operations in the CKKS scheme. For that, the error variance is minimized instead of the upper bound of error when we deal with the encrypted data. Second, from the same perspective of minimizing error variance, I propose a new method to find the approximate polynomials for the CKKS scheme. The approximation method is especially applied to the CKKS scheme's bootstrapping, where we achieve bootstrapping with smaller error variance compared to the prior arts. In addition to the above variance-minimizing method, I cast the problem of finding an approximate polynomial for a modulus reduction into an L2-norm minimization problem. As a result, I find an approximate polynomial for the modulus reduction without using the sine function, which is the upper bound for the polynomial approximation of the modulus reduction. By using the proposed method, the constraint of q = O(m^{3/2}) is relaxed as O(m), and thus the level loss in bootstrapping can be reduced. The performance improvement by the proposed methods is verified by implementation over HE libraries, that is, HEAAN and SEAL. The implementation shows that by reordering homomorphic operations and using the proposed polynomial approximation, the reliability of the CKKS scheme is improved. Therefore, the quality of services of various applications using the proposed CKKS scheme, such as PPML, can be improved without compromising performance and security. Finally, I propose an improved code-based signature scheme and cryptanalysis of code-based cryptosystems. A novel code-based signature scheme with small parameters and an attack algorithm on recent code-based cryptosystems are presented in this dissertation. This scheme is based on a modified Reed-Muller (RM) code, which reduces the signing complexity and key size compared with existing code-based signature schemes. The proposed scheme has the advantage of the pqsigRM decoder and uses public codes that are more difficult to distinguish from random codes. I use (U, U+V) -codes with the high-dimensional hull to overcome the disadvantages of code-based schemes. The proposed a decoder which efficiently samples from coset elements with small Hamming weight for any given syndrome. The proposed signature scheme resists various known attacks on RM code-based cryptography. For 128 bits of classical security, the signature size is 4096 bits, and the public key size is less than 1 MB. Recently, Ivanov, Kabatiansky, Krouk, and Rumenko (IKKR) proposed three new variants of the McEliece cryptosystem (CBCrypto 2020, affiliated with Eurocrypt 2020). This dissertation shows that one of the IKKR cryptosystems is equal to the McEliece cryptosystem. Furthermore, a polynomial-time attack algorithm for the other two IKKR cryptosystems is proposed. The proposed attack algorithm utilizes the linearity of IKKR cryptosystems. Also, an implementation of the IKKR cryptosystems and the proposed attack is given. The proposed attack algorithm finds the plaintext within 0.2 sec, which is faster than the elapsed time for legitimate decryption.본 논문은 크게 다음의 세 가지의 기여를 포함한다. i) 네트워크를 활용해서 정보 보호 딥러닝을 개선하는 프로토콜 ii) 근사 동형 암호에서 보안성과 성능의 손해 없이 에러를 낮추고 높은 정확도로 부트스트래핑 하는 방법 iii) IKKR 암호 시스템과 pqsigRM 등 부호 기반 암호를 공격하는 방법과 효율적인 부호 기반 전자 서명 시스템. 근래의 기계학습과 블록체인 기술의 발전으로 인해서 기밀 데이터에 대한 연산을 어떻게 외주할 수 있느냐에 대한 새로운 보안 문제가 대두되고 있다. 또한, 양자 컴퓨터에 관한 연구가 성공을 거듭하면서, 이를 이용한 공격에 저항하는 포스트 양자 암호의 필요성 또한 커지고 있다. 다자간 컴퓨팅은 데이터를 공개하지 않고 데이터에 대한 연산을 수행할 수 있도록 하는 암호학적 프로토콜의 총칭이다. 다자간 컴퓨팅은 동형 암호와 포스트 양자 암호에 기반하고 있으므로, 효율적인 동형 암호와 포스트 양자 암호에 관한 연구가 활발하게 수행되고 있다. 동형 암호는 암호화된 데이터에 대한 연산이 가능한 특수한 암호화 알고리즘이다. 일반적으로 동형 암호의 암호문에 대해서 수행 가능한 연산의 깊이가 정해져 있으며, 이를 암호문의 레벨이라고 칭한다. 레벨을 모두 소비한 암호문의 레벨을 다시 복원하는 과정을 부트스트래핑 (bootstrapping)이라고 칭한다. 일반적으로 부트스트래핑은 매우 오래 걸리는 연산이며 시간 및 공간 복잡도가 크다. 그러나, 딥러닝과 같이 깊이가 큰 연산을 수행하는 경우 부트스트래핑이 필수적이다. 본 논문에서는 정보 보호 기계학습을 위한 새로운 프로토콜을 제안한다. 이 프로토콜에서는 입력 메시지와 더불어 신경망의 중간값들 또한 안전하게 보호된다. 그러나 여전히 사용자의 통신 및 연산 복잡도는 낮게 유지된다. Cheon, Kim, Kim 그리고 Song (CKKS)가 제안한 암호 시스템 (Asiacrypt 17)은 기계학습 등에서 가장 널리 쓰이는 데이터인 실수를 효율적으로 다룰 수 있으므로 가장 촉망받는 완전 동형 암호 시스템이다. 그러나, 오류의 증폭과 전파가 CKKS 암호 시스템의 가장 큰 단점이다. 이 논문에서는 아래의 기술을 활용하여 CKKS 암호 시스템의 오류를 줄이는 방법을 제안하며, 이는 근사 동형 암호에 일반화하여 적용할 수 있다. 첫째, 신호 대비 잡음 비 (signal-to-noise ratio, SNR)의 개념을 도입하여, SNR를 최대화하도록 연산의 순서를 재조정한다. 그러기 위해서는, 오류의 최대치 대신 분산이 최소화되어야 하며, 이를 관리해야 한다. 둘째, 오류의 분산을 최소화한다는 같은 관점에서 새로운 다항식 근사 방법을 제안한다. 이 근사 방법은 특히, CKKS 암호 시스템의 부트스트래핑에 적용되었으며, 종래 기술보다 더 낮은 오류를 달성한다. 위의 방법에 더하여, 근사 다항식을 구하는 문제를 L2-norm 최소화 문제로 치환하는 방법을 제안한다. 이를 통해서 사인 함수의 도입 없이 근사 다항식을 구하는 방법을 제안한다. 제안된 방법을 사용하면, q=O(m^{3/2})라는 제약을 q=O(m)으로 줄일 수 있으며, 부트스트래핑에 필요한 레벨 소모를 줄일 수 있다. 성능 향상은 HEAAN과 SEAL 등의 동형 암호 라이브러리를 활용한 구현을 통해 증명했으며, 구현을 통해서 연산 재정렬과 새로운 부트스트래핑이 CKKS 암호 시스템의 성능을 향상함을 확인했다. 따라서, 보안성과 성능의 타협 없이 근사 동형 암호를 사용하는 서비스의 질을 향상할 수 있다. 양자 컴퓨터를 활용하여 전통적인 공개키 암호를 공격하는 효율적인 알고리즘이 공개되면서, 포스트 양자 암호에 대한 필요성이 증대했다. 부호 기반 암호는 포스트 양자 암호로써 널리 연구되었다. 작은 키 크기를 갖는 새로운 부호 기반 전자 서명 시스템과 부호 기반 암호를 공격하는 방법이 논문에 제안되어 있다. pqsigRM이라 명명한 전자 서명 시스템이 그것이다. 이 전자 서명 시스템은 수정된 Reed-Muller (RM) 부호를 활용하며, 서명의 복잡도와 키 크기를 종래 기술보다 많이 줄인다. pqsigRM은 hull의 차원이 큰 (U, U+V) 부호와 이의 복호화를 이용하여, 서명에서 큰 이득이 있다. 이 복호화 알고리즘은 주어진 모든 코셋 (coset)의 원소에 대하여 작은 헤밍 무게를 갖는 원소를 반환한다. 또한, 수정된 RM 부호를 이용하여, 알려진 모든 공격에 저항한다. 128비트 안정성에 대해서 서명의 크기는 4096 비트이고, 공개 키의 크기는 1MB보다 작다. 최근, Ivanov, Kabatiansky, Krouk, 그리고 Rumenko (IKKR)가 McEliece 암호 시스템의 세 가지 변형을 발표했다 (CBCrypto 2020, Eurocrypt 2020와 함께 진행). 본 논문에서는 IKKR 암호 시스템중 하나가 McEliece 암호 시스템과 동치임을 증명한다. 또한 나머지 IKKR 암호 시스템에 대한 다항 시간 공격을 제안한다. 제안하는 공격은 IKKR 암호 시스템의 선형성을 활용한다. 또한, 이 논문은 제안한 공격의 구현을 포함하며, 제안된 공격은 0.2초 이내에 메시지를 복원하고, 이는 정상적인 복호화보다 빠른 속도이다.Contents Abstract i Contents iv List of Tables ix List of Figures xi 1 Introduction 1 1.1 Homomorphic Encryption and Privacy-Preserving Machine Learning 4 1.2 High-Precision CKKS Scheme and Its Bootstrapping 5 1.2.1 Near-Optimal Bootstrapping of the CKKS Scheme Using Least Squares Method 6 1.2.2 Variance-Minimizing and Optimal Bootstrapping of the CKKS Scheme 8 1.3 Efficient Code-Based Signature Scheme and Cryptanalysis of the Ivanov-Kabatiansky-Krouk-Rumenko Cryptosystems 10 1.3.1 Modified pqsigRM: An Efficient Code-Based Signature Scheme 11 1.3.2 Ivanov-Kabatiansky-Krouk-Rumenko Cryptosystems and Its Equality 13 1.4 Organization of the Dissertation 14 2 Preliminaries 15 2.1 Basic Notation 15 2.2 Privacy-Preserving Machine Learning and Security Terms 16 2.2.1 Privacy-Preserving Machine Learning and Security Terms 16 2.2.2 Privacy-Preserving Machine Learning 17 2.3 The CKKS Scheme and Its Bootstrapping 18 2.3.1 The CKKS Scheme 18 2.3.2 CKKS Scheme in RNS 22 2.3.3 Bootstrapping of the CKKS Scheme 24 2.3.4 Statistical Characteristics of Modulus Reduction and Failure Probability of Bootstrapping of the CKKS Scheme 26 2.4 Approximate Polynomial and Signal-to-Noise Perspective for Approximate Homomorphic Encryption 27 2.4.1 Chebyshev Polynomials 27 2.4.2 Signal-to-Noise Perspective of the CKKS Scheme 28 2.5 Preliminary for Code-Based Cryptography 29 2.5.1 The McEliece Cryptosystem 29 2.5.2 CFS Signature Scheme 30 2.5.3 ReedMuller Codes and Recursive Decoding 31 2.5.4 IKKR Cryptosystems 33 3 Privacy-Preserving Machine Learning via FHEWithout Bootstrapping 37 3.1 Introduction 37 3.2 Information Theoretic Secrecy and HE for Privacy-Preserving Machine Learning 38 3.2.1 The Failure Probability of Ordinary CKKS Bootstrapping 39 3.3 Comparison With Existing Methods 43 3.3.1 Comparison With the Hybrid Method 43 3.3.2 Comparison With FHE Method 44 3.4 Comparison for Evaluating Neural Network 45 4 High-Precision Approximate Homomorphic Encryption and Its Bootstrapping by Error Variance Minimization and Convex Optimization 50 4.1 Introduction 50 4.2 Optimization of Error Variance in the Encrypted Data 51 4.2.1 Tagged Information for Ciphertext 52 4.2.2 WorstCase Assumption 53 4.2.3 Error in Homomorphic Operations of the CKKS Scheme 54 4.2.4 Reordering Homomorphic Operations 59 4.3 Near-Optimal Polynomial for Modulus Reduction 66 4.3.1 Approximate Polynomial Using L2-Norm optimization 66 4.3.2 Efficient Homomorphic Evaluation of the Approximate Polynomial 70 4.4 Optimal Approximate Polynomial and Bootstrapping of the CKKS Scheme 73 4.4.1 Polynomial Basis Error and Polynomial Evaluation in the CKKS Scheme 73 4.4.2 Variance-Minimizing Polynomial Approximation 74 4.4.3 Optimal Approximate Polynomial for Bootstrapping and Magnitude of Its Coefficients 75 4.4.4 Reducing Complexity and Error Using Odd Function 79 4.4.5 Generalization of Weight Constants and Numerical Method 80 4.5 Comparison and Implementation 84 4.6 Reduction of Level Loss in Bootstrapping 89 4.7 Implementation of the Proposed Method and Performance Comparison 92 4.7.1 Error Variance Minimization 92 4.7.2 Weight Constant and Minimum Error Variance 93 4.7.3 Comparison of the Proposed MethodWith the Previous Methods 96 5 Efficient Code-Based Signature Scheme and Cryptanalysis of Code-Based Cryptosystems 104 5.1 Introduction 104 5.2 Modified ReedMuller Codes and Proposed Signature Scheme 105 5.2.1 Partial Permutation of Generator Matrix and Modified ReedMuller Codes 105 5.2.2 Decoding of Modified ReedMuller Codes 108 5.2.3 Proposed Signature Scheme 110 5.3 Security Analysis of Modified pqsigRM 111 5.3.1 Decoding One Out of Many 112 5.3.2 Security Against Key Substitution Attacks 114 5.3.3 EUFCMA Security 114 5.4 Indistinguishability of the Public Code and Signature 120 5.4.1 Modifications of Public Code 121 5.4.2 Public Code Indistinguishability 124 5.4.3 Signature Leaks 126 5.5 Parameter Selection 126 5.5.1 Parameter Sets 126 5.5.2 Statistical Analysis for Determining Number of Partial Permutations 128 5.6 Equivalence of the Prototype IKKR and the McEliece Cryptosystems 131 5.7 Cryptanalysis of the IKKR Cryptosystems 133 5.7.1 Linearity of Two Variants of IKKR Cryptosystems 133 5.7.2 The Attack Algorithm 134 5.7.3 Implementation 135 6 Conclusion 139 6.1 Privacy-Preserving Machine Learning Without Bootstrapping 139 6.2 Variance-Minimization in the CKKS Scheme 140 6.3 L2-Norm Minimization for the Bootstrapping of the CKKS Scheme 141 6.4 Modified pqsigRM: RM Code-Based Signature Scheme 142 6.5 Cryptanalysis of the IKKR Cryptosystem 143 Abstract (In Korean) 155 Acknowlegement 158Docto

Multilinear Maps in Cryptography

Multilineare Abbildungen spielen in der modernen Kryptographie eine immer bedeutendere Rolle. In dieser Arbeit wird auf die Konstruktion, Anwendung und Verbesserung von multilinearen Abbildungen eingegangen

Privacy-preserving information hiding and its applications

The phenomenal advances in cloud computing technology have raised concerns about data privacy. Aided by the modern cryptographic techniques such as homomorphic encryption, it has become possible to carry out computations in the encrypted domain and process data without compromising information privacy. In this thesis, we study various classes of privacy-preserving information hiding schemes and their real-world applications for cyber security, cloud computing, Internet of things, etc. Data breach is recognised as one of the most dreadful cyber security threats in which private data is copied, transmitted, viewed, stolen or used by unauthorised parties. Although encryption can obfuscate private information against unauthorised viewing, it may not stop data from illegitimate exportation. Privacy-preserving Information hiding can serve as a potential solution to this issue in such a manner that a permission code is embedded into the encrypted data and can be detected when transmissions occur. Digital watermarking is a technique that has been used for a wide range of intriguing applications such as data authentication and ownership identification. However, some of the algorithms are proprietary intellectual properties and thus the availability to the general public is rather limited. A possible solution is to outsource the task of watermarking to an authorised cloud service provider, that has legitimate right to execute the algorithms as well as high computational capacity. Privacypreserving Information hiding is well suited to this scenario since it is operated in the encrypted domain and hence prevents private data from being collected by the cloud. Internet of things is a promising technology to healthcare industry. A common framework consists of wearable equipments for monitoring the health status of an individual, a local gateway device for aggregating the data, and a cloud server for storing and analysing the data. However, there are risks that an adversary may attempt to eavesdrop the wireless communication, attack the gateway device or even access to the cloud server. Hence, it is desirable to produce and encrypt the data simultaneously and incorporate secret sharing schemes to realise access control. Privacy-preserving secret sharing is a novel research for fulfilling this function. In summary, this thesis presents novel schemes and algorithms, including: • two privacy-preserving reversible information hiding schemes based upon symmetric cryptography using arithmetic of quadratic residues and lexicographic permutations, respectively. • two privacy-preserving reversible information hiding schemes based upon asymmetric cryptography using multiplicative and additive privacy homomorphisms, respectively. • four predictive models for assisting the removal of distortions inflicted by information hiding based respectively upon projection theorem, image gradient, total variation denoising, and Bayesian inference. • three privacy-preserving secret sharing algorithms with different levels of generality

Cloud-based homomorphic encryption for privacy-preserving machine learning in clinical decision support

While privacy and security concerns dominate public cloud services, Homomorphic Encryption (HE) is seen as an emerging solution that ensures secure processing of sensitive data via untrusted networks in the public cloud or by third-party cloud vendors. It relies on the fact that some encryption algorithms display the property of homomorphism, which allows them to manipulate data meaningfully while still in encrypted form; although there are major stumbling blocks to overcome before the technology is considered mature for production cloud environments. Such a framework would find particular relevance in Clinical Decision Support (CDS) applications deployed in the public cloud. CDS applications have an important computational and analytical role over confidential healthcare information with the aim of supporting decision-making in clinical practice. Machine Learning (ML) is employed in CDS applications that typically learn and can personalise actions based on individual behaviour. A relatively simple-to-implement, common and consistent framework is sought that can overcome most limitations of Fully Homomorphic Encryption (FHE) in order to offer an expanded and flexible set of HE capabilities. In the absence of a significant breakthrough in FHE efficiency and practical use, it would appear that a solution relying on client interactions is the best known entity for meeting the requirements of private CDS-based computation, so long as security is not significantly compromised. A hybrid solution is introduced, that intersperses limited two-party interactions amongst the main homomorphic computations, allowing exchange of both numerical and logical cryptographic contexts in addition to resolving other major FHE limitations. Interactions involve the use of client-based ciphertext decryptions blinded by data obfuscation techniques, to maintain privacy. This thesis explores the middle ground whereby HE schemes can provide improved and efficient arbitrary computational functionality over a significantly reduced two-party network interaction model involving data obfuscation techniques. This compromise allows for the powerful capabilities of HE to be leveraged, providing a more uniform, flexible and general approach to privacy-preserving system integration, which is suitable for cloud deployment. The proposed platform is uniquely designed to make HE more practical for mainstream clinical application use, equipped with a rich set of capabilities and potentially very complex depth of HE operations. Such a solution would be suitable for the long-term privacy preserving-processing requirements of a cloud-based CDS system, which would typically require complex combinatorial logic, workflow and ML capabilities

A survey of machine and deep learning methods for privacy protection in the Internet of things

Recent advances in hardware and information technology have accelerated the proliferation of smart and interconnected devices facilitating the rapid development of the Internet of Things (IoT). IoT applications and services are widely adopted in environments such as smart cities, smart industry, autonomous vehicles, and eHealth. As such, IoT devices are ubiquitously connected, transferring sensitive and personal data without requiring human interaction. Consequently, it is crucial to preserve data privacy. This paper presents a comprehensive survey of recent Machine Learning (ML)- and Deep Learning (DL)-based solutions for privacy in IoT. First, we present an in depth analysis of current privacy threats and attacks. Then, for each ML architecture proposed, we present the implementations, details, and the published results. Finally, we identify the most effective solutions for the different threats and attacks.This work is partially supported by the Generalitat de Catalunya under grant 2017 SGR 962 and the HORIZON-GPHOENIX (101070586) and HORIZON-EUVITAMIN-V (101093062) projects.Peer ReviewedPostprint (published version