Privacy-Preserving and Lightweight Selective Aggregation with Fault-Tolerance for Edge Computing-Enhanced IoT

Area of Science:

• Computer Science
• Network Security
• Distributed Systems

Background:

• Internet of Things (IoT) applications require edge computing for efficient data processing.
• Existing data aggregation schemes in IoT often lack filtering, compromising data privacy and fault tolerance.
• Challenges include protecting individual IoT device data privacy and ensuring lightweight, fault-tolerant schemes.

Purpose of the Study:

• To propose a novel privacy-preserving and lightweight selective aggregation scheme with fault tolerance (PLSA-FT) for edge computing-enhanced IoT.
• To enable selective data aggregation based on cloud center queries while ensuring data privacy and device reliability.
• To address the limitations of existing aggregation methods concerning privacy, fault tolerance, and computational overhead.

Main Methods:

• Developed a selective aggregation mechanism using Boolean and numerical responses tailored to cloud queries.
• Modified the Paillier homomorphic encryption to ensure data privacy and support fault tolerance for IoT devices.
• Implemented an online/offline signature mechanism to minimize computational costs.

Main Results:

• The PLSA-FT scheme demonstrates confidentiality, privacy preservation, source authentication, and integrity verification.
• The scheme provides robust fault tolerance and supports dynamic membership management for IoT devices.
• Performance evaluations confirm PLSA-FT is lightweight, featuring low computation costs and communication overhead.

Conclusions:

• PLSA-FT effectively enhances security and efficiency in edge computing-enabled IoT environments.
• The proposed scheme offers a practical solution for privacy-preserving and fault-tolerant data aggregation.
• PLSA-FT meets the demanding requirements of modern IoT applications through its innovative design.