Privacy-Preserving Chaotic Extreme Learning Machine with Fully Homomorphic Encryption

The Machine Learning and Deep Learning Models require a lot of data for the training process, and in some scenarios, there might be some sensitive data, such as customer information involved, which the organizations might be hesitant to outsource for model building. Some of the privacy-preserving techniques such as Differential Privacy, Homomorphic Encryption, and Secure Multi-Party Computation can be integrated with different Machine Learning and Deep Learning algorithms to provide security to the data as well as the model. In this paper, we propose a Chaotic Extreme Learning Machine and its encrypted form using Fully Homomorphic Encryption where the weights and biases are generated using a logistic map instead of uniform distribution. Our proposed method has performed either better or similar to the Traditional Extreme Learning Machine on most of the datasets.Comment: 26 pages; 1 Figure; 7 Tables. arXiv admin note: text overlap with arXiv:2205.1326

Encrypted statistical machine learning: new privacy preserving methods

We present two new statistical machine learning methods designed to learn on fully homomorphic encrypted (FHE) data. The introduction of FHE schemes following Gentry (2009) opens up the prospect of privacy preserving statistical machine learning analysis and modelling of encrypted data without compromising security constraints. We propose tailored algorithms for applying extremely random forests, involving a new cryptographic stochastic fraction estimator, and na\"{i}ve Bayes, involving a semi-parametric model for the class decision boundary, and show how they can be used to learn and predict from encrypted data. We demonstrate that these techniques perform competitively on a variety of classification data sets and provide detailed information about the computational practicalities of these and other FHE methods.Comment: 39 page

Exploring Machine Learning Models for Federated Learning: A Review of Approaches, Performance, and Limitations

In the growing world of artificial intelligence, federated learning is a distributed learning framework enhanced to preserve the privacy of individuals' data. Federated learning lays the groundwork for collaborative research in areas where the data is sensitive. Federated learning has several implications for real-world problems. In times of crisis, when real-time decision-making is critical, federated learning allows multiple entities to work collectively without sharing sensitive data. This distributed approach enables us to leverage information from multiple sources and gain more diverse insights. This paper is a systematic review of the literature on privacy-preserving machine learning in the last few years based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Specifically, we have presented an extensive review of supervised/unsupervised machine learning algorithms, ensemble methods, meta-heuristic approaches, blockchain technology, and reinforcement learning used in the framework of federated learning, in addition to an overview of federated learning applications. This paper reviews the literature on the components of federated learning and its applications in the last few years. The main purpose of this work is to provide researchers and practitioners with a comprehensive overview of federated learning from the machine learning point of view. A discussion of some open problems and future research directions in federated learning is also provided

Confidential Boosting with Random Linear Classifiers for Outsourced User-generated Data

User-generated data is crucial to predictive modeling in many applications. With a web/mobile/wearable interface, a data owner can continuously record data generated by distributed users and build various predictive models from the data to improve their operations, services, and revenue. Due to the large size and evolving nature of users data, data owners may rely on public cloud service providers (Cloud) for storage and computation scalability. Exposing sensitive user-generated data and advanced analytic models to Cloud raises privacy concerns. We present a confidential learning framework, SecureBoost, for data owners that want to learn predictive models from aggregated user-generated data but offload the storage and computational burden to Cloud without having to worry about protecting the sensitive data. SecureBoost allows users to submit encrypted or randomly masked data to designated Cloud directly. Our framework utilizes random linear classifiers (RLCs) as the base classifiers in the boosting framework to dramatically simplify the design of the proposed confidential boosting protocols, yet still preserve the model quality. A Cryptographic Service Provider (CSP) is used to assist the Cloud's processing, reducing the complexity of the protocol constructions. We present two constructions of SecureBoost: HE+GC and SecSh+GC, using combinations of homomorphic encryption, garbled circuits, and random masking to achieve both security and efficiency. For a boosted model, Cloud learns only the RLCs and the CSP learns only the weights of the RLCs. Finally, the data owner collects the two parts to get the complete model. We conduct extensive experiments to understand the quality of the RLC-based boosting and the cost distribution of the constructions. Our results show that SecureBoost can efficiently learn high-quality boosting models from protected user-generated data

Exploring Privacy-Preserving Disease Diagnosis: A Comparative Analysis

In the healthcare sector, data is considered as a valuable asset, with enormous amounts generated in the form of patient records and disease-related information. Leveraging machine learning techniques enables the analysis of extensive datasets, unveiling hidden patterns in diseases, facilitating personalized treatments, and forecasting potential health issues. However, the flourish of online diagnosis and prediction still faces some challenges related to information security and privacy as disease diagnosis technologies utilizes a lot of clinical records and sensitive patient data. Hence, it becomes imperative to prioritize the development of innovative methodologies that not only advance the accuracy and efficiency of disease prediction but also ensure the highest standards of privacy protection. This requires collaborative efforts between researchers, healthcare practitioners, and policymakers to establish a comprehensive framework that addresses the evolving landscape of healthcare data while safeguarding individual privacy. Addressing this constraint, numerous researchers integrate privacy preservation measures with disease prediction techniques to develop a system capable of diagnosing diseases without compromising the confidentiality of sensitive information. The survey paper conducts a comparative analysis of privacy-preserving techniques employed in disease diagnosis and prediction. It explores existing methodologies across various domains, assessing their efficacy and trade-offs in maintaining data confidentiality while optimizing diagnostic accuracy. The review highlights the need for robust privacy measures in disease prediction, shortcomings related to existing techniques of privacy preserving disease diagnosis, and provides insights into promising directions for future research in this critical intersection of healthcare and privacy preservation