Privacy-Preserving Techniques in Cloud/Fog and Internet of Things

Lee, Cheng-Chi; Gheisari, Mehdi; Shayegan, Mohammad Javad; Ahvanooey, Milad Taleby; Liu, Yang

doi:10.3390/cryptography7040051

Cryptography

2023

DOI: 10.3390/cryptography7040051

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Privacy-Preserving Techniques in Cloud/Fog and Internet of Things

Cheng-Chi Lee,

Mehdi Gheisari,

Mohammad Javad Shayegan

et al.

Abstract: Recently, wireless networks have been developed using cloud infrastructure and software-based networks [...]

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2024

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

Vashishth,

Sharma,

Sharma

et al. 2024

Emerging Technologies for Securing the Cloud and IoT

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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.

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

Vashishth,

Sharma,

Sharma

et al. 2024

Emerging Technologies for Securing the Cloud and IoT

View full text Add to dashboard Cite

show abstract

Applications of Machine Learning Algorithms in Data Encryption Standards

V.,

2024

Machine Learning and Cryptographic Solutions for Data Protection and Network Security

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Encryption plays a crucial role in safeguarding sensitive information in today's digital world. The traditional encryption methods rely on mathematical algorithms, such as RSA and AES, for securing data. The proliferation of digital communication and the increasing need for secure data transmission have prompted significant advancements in encryption techniques. As data breaches and cyber threats become more sophisticated, there is an increasing need for robust encryption techniques. Machine learning algorithms, with their ability to adapt and learn from data patterns, have emerged as a valuable tool in enhancing encryption processes. This chapter explores the applications of machine learning algorithms in encryption, highlighting their potential to improve security, speed, and versatility. The authors delve into various aspects, including data encryption, key management, authentication, and intrusion detection, demonstrating how machine learning can contribute to the development of more robust and efficient encryption systems.

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Privacy-Preserving Techniques in Cloud/Fog and Internet of Things

Abstract: Recently, wireless networks have been developed using cloud infrastructure and software-based networks [...]

Cited by 2 publications

References 10 publications

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

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

Applications of Machine Learning Algorithms in Data Encryption Standards

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