The chapter begins by highlighting the growing concerns around user privacy in the rapidly expanding IoT ecosystem, which generates massive amounts of unsecured data. It then provides an overview of some existing survey works that have reviewed privacy issues and threats in IoT environments.
The chapter then delves into the various privacy preservation schemes proposed in the literature. It first discusses centralized architecture-based encryption techniques, such as homomorphic encryption, attribute access control, and multi-party computation, which aim to protect user data privacy while enabling third-party computations.
Next, the chapter explores distributed learning-based solutions, where the learning models are generated at each participant device, with a central server coordinating the process. These schemes leverage the principles of distributed machine learning to preserve user privacy by avoiding the need to share raw data.
The chapter then examines schemes that integrate distributed learning with encryption mechanisms, such as homomorphic encryption and differential privacy. These hybrid approaches aim to further enhance the privacy guarantees by combining the benefits of distributed learning and cryptographic techniques.
The chapter concludes by highlighting some emerging trends and future research directions in the field of data privacy preservation for the IoT, including the need for more efficient and precise systems, better evaluation of privacy solutions in real-world scenarios, and effective standardization efforts by relevant bodies.
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by Jaydip Sen,J... at arxiv.org 04-02-2024
https://arxiv.org/pdf/2404.00235.pdfDeeper Inquiries