Privacy-Preserving Machine Learning Techniques for IoT Data in Cloud Environments

Abstract

The proliferation of internet of things (IoT) devices has resulted in an unprecedented influx of data, leading to heightened concerns regarding the privacy and security of sensitive information in cloud environments. Privacy-preserving machine learning techniques have emerged as essential tools for ensuring the confidentiality of IoT data while facilitating meaningful analysis. This chapter provides an overview of the key principles and methodologies employed in privacy-preserving machine learning for IoT data in cloud environments. Key considerations encompass data anonymization, secure transmission, and adherence to stringent data protection regulations such as the General Data Protection Regulation (GDPR). Robust encryption and access control mechanisms are implemented to safeguard data integrity while allowing for effective analysis. Techniques like homomorphic encryption and secure multi-party computation enable secure computations on encrypted data, ensuring privacy while maintaining the utility of the data.