Modern privacy preserving strategies for IoT security

Title

Modern privacy preserving strategies for IoT security

Creator

Dash, Adarsh Kumar; Sehgal, Chiranjeev; Dey, Hemangshu; Ramadani, Lauresha; Rajesh Kanna, R.

Description

The proliferation of Internet of Things (IoT) devices has brought about a revolution in various industries and everyday life, enhancing connectivity and efficiency. Nevertheless, this rapid adoption has also given rise to notable security and privacy challenges, leading to the need for robust solutions to safeguard sensitive data. This study delves into contemporary strategies for preserving privacy specifically designed for IoT security, with a focus on the most recent trends and technologies. By the year 2024, it is projected that the global count of IoT devices will exceed 30 billion, exhibiting a compound annual growth rate (CAGR) of 26.7% from 2021 to 2024. This exponential growth has led to a significant increase in the amount of data produced and transmitted by IoT devices, thereby creating fresh opportunities as well as vulnerabilities. Privacy apprehensions are crucial, given that these devices frequently amass sensitive personal and organizational data. The research scrutinizes cutting-edge privacy-preserving techniques, such as federated learning, homomorphic encryption, and differential privacy, which present promising resolutions for safeguarding data while upholding functionality. Federated learning has garnered attention as a decentralized approach that permits data processing to occur locally on devices rather than being sent to central servers, thereby reducing data exposure. Homomorphic encryption facilitates data processing while encrypted, ensuring a high level of security without disclosing the underlying information. Conversely, differential privacy introduces statistical noise to data, guaranteeing that individual data points are not easily discernible, thus preserving user privacy. This section also accentuates the significance of adhering to regulations and the impact of frameworks like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) on shaping the advancement and acceptance of privacy-preserving technologies. Moreover, an exploration is made into the incorporation of blockchain for immutable and transparent data management in IoT environments. This manuscript furnishes an exhaustive overview of the prevalent trends and technologies within this realm, providing insights into the future trajectory of IoT security and privacy. 2026 Elsevier Inc. All rights reserved..

Source

IoT Security: Fundamentals and Key Enabling Technologies;pp.125-136

Date

01-01-2025

Publisher

Elsevier

Subject

Computer security; Computer security and privacy; Cryptography; Information systems; Network (computer science); Operations management

Coverage

Dash A.K., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India; Sehgal C., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India; Dey H., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India; Ramadani L., Computer Science Faculty, AAB College Prishtina, Kosovo, Prishtina; Rajesh Kanna R., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 978-044334125-0; 978-044334126-7;

Format

online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.1016/B978-0-443-34125-0.00003-9

https://www.scopus.com/pages/publications/105026966910?origin=resultslist