Data Mining Practical Machine Learning Tools and Techniques

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

Advances in Cryptology - ASIACRYPT 2004

Emerging neural networks based machine learning techniques such as deep learning and its variants have shown tremendous potential in many application domains. However, they raise serious privacy concerns due to the risk of leakage of highly privacy-sensitive data when data collected from users is used to train neural network models to support predictive tasks. To tackle such serious privacy concerns, several privacy-preserving approaches have been proposed in the literature that use either secure multi-party computation (SMC) or homomorphic encryption (HE) as the underlying mechanisms. However, neither of these cryptographic approaches provides an efficient solution towards constructing a privacy-preserving machine learning model, as well as supporting both the training and inference phases. To tackle the above issue, we propose a CryptoNN framework that supports training a neural network model over encrypted data by using the emerging functional encryption scheme instead of SMC or HE. We also construct a functional encryption scheme for basic arithmetic computation to support the requirement of the proposed CryptoNN framework. We present performance evaluation and security analysis of the underlying crypto scheme and show through our experiments that CryptoNN achieves accuracy that is similar to those of the baseline neural network models on the MNIST dataset.

CryptoNN: Training Neural Networks over Encrypted Data

Data privacy concerns are increasing significantly in the context of the Internet of Things, cloud services, edge computing, artificial intelligence applications, and other applications enabled by next-generation networks. Homomorphic encryption addresses privacy challenges by enabling multiple operations to be performed on encrypted messages without decryption. This article comprehensively addresses homomorphic encryption from both theoretical and practical perspectives. This article delves into the mathematical foundations required to understand fully homomorphic encryption ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textsf {FHE}$ </tex-math></inline-formula> ). It consequently covers design fundamentals and security properties of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textsf {FHE}$ </tex-math></inline-formula> and describes the main <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textsf {FHE}$ </tex-math></inline-formula> schemes based on various mathematical problems. On a more practical level, this article presents a view on privacy-preserving machine learning using homomorphic encryption and then surveys <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textsf {FHE}$ </tex-math></inline-formula> at length from an engineering angle, covering the potential application of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textsf {FHE}$ </tex-math></inline-formula> in fog computing and cloud computing services. It also provides a comprehensive analysis of existing state-of-the-art <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textsf {FHE}$ </tex-math></inline-formula> libraries and tools, implemented in software and hardware, and the performance thereof. 

https://zenodo.org/record/7152464/files/FHE%20survey.pdf

Survey on Fully Homomorphic Encryption, Theory, and Applications

/pdf/improved-programmable-bootstrapping-with-larger-precision-5fznsi7sx8.pdf

Improved Programmable Bootstrapping with Larger Precision and Efficient Arithmetic Circuits for TFHE.

In the context of data outsourcing more and more concerns raise about the privacy of user’s data. Simultaneously, cryptographers are designing schemes enabling computation on ciphertexts (homomorphic encryption, functional encryption, etc.). Their use in real world applications is difficult. In this work we focus on functional encryption schemes enabling computation of inner-product on encrypted vectors and their use in real world scenarios. We propose a protocol combining such type of functional encryption schemes with machine learning algorithms. Indeed, we think that being able to perform classification over encrypted data is useful in many scenarios, in particular when the owners of the data are not ready to share it. After explaining our protocol, we detail the implemented handwritten digit recognition use case, and then, we study its security.

Privacy Preserving Data Classification using Inner-product Functional Encryption

Abstract Classification algorithms/tools become more and more powerful and pervasive. Yet, for some use cases, it is necessary to be able to protect data privacy while benefiting from the functionalities they provide. Among the tools that may be used to ensure such privacy, we are focusing in this paper on functional encryption. These relatively new cryptographic primitives enable the evaluation of functions over encrypted inputs, outputting cleartext results. Theoretically, this property makes them well-suited to process classification over encrypted data in a privacy by design’ rationale, enabling to perform the classification algorithm over encrypted inputs (i.e. without knowing the inputs) while only getting the input classes as a result in the clear. In this paper, we study the security and privacy issues of classifiers using today practical functional encryption schemes. We provide an analysis of the information leakage about the input data that are processed in the encrypted domain with state-of-the-art functional encryption schemes. This study, based on experiments ran on MNIST and Census Income datasets, shows that neural networks are able to partially recover information that should have been kept secret. Hence, great care should be taken when using the currently available functional encryption schemes to build privacy-preserving classification services. It should be emphasized that this work does not attack the cryptographic security of functional encryption schemes, it rather warns the community against the fact that they should be used with caution for some use cases and that the current state-ofthe-art may lead to some operational weaknesses that could be mitigated in the future once more powerful functional encryption schemes are available.

https://www.sciendo.com/pdf/10.2478/popets-2020-0015

Illuminating the Dark or how to recover what should not be seen in FE-based classifiers

In this work, we study the practical security of innerproduct functional encryption. We left behind the mathematical security proof of the schemes, provided in the literature, and focus on what attackers can use in realistic scenarios without tricking the protocol, and how they can retrieve more than they should be able to. This study is based on the proposed protocol from [1]. We generalize the scenario to an attacker possessing n secret keys. We propose attacks based on machine learning, and experiment them over the MNIST dataset [2].

Information Leakage Analysis of Inner-Product Functional Encryption Based Data Classification

Illuminating the Dark or how to recover what should not be seen.

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