Privacy-Preserving Multi-User Graph Intersection Scheme for Wireless Communications in Cloud-Assisted Internet of...

Area of Science:

• Computer Science
• Cybersecurity
• Cloud Computing

Background:

• Cloud-assisted Internet of Things (IoT) is crucial for smart societies, addressing traditional IoT limitations.
• Graph databases and cloud-IoT advancements drive research in privacy-preserving graph computations.
• Existing single-user graph intersection schemes are inefficient and insecure for multi-user cloud-IoT environments.

Purpose of the Study:

• To propose a secure and efficient graph intersection scheme for multi-user queries in cloud-assisted IoT.
• To address the high computational/communication costs and key leakage risks of existing methods.
• To enable flexible, privacy-preserving graph intersection queries without repeated data owner encryption.

Main Methods:

• Implemented a secure graph intersection scheme utilizing proxy re-encryption (PRE).
• Designed the scheme for cloud-assisted IoT environments to support multi-user intersection queries.
• Data owners encrypt graph data once; PRE transforms it for authorized users to decrypt with their private keys.

Main Results:

• The proposed scheme supports flexible, multi-user graph intersection queries.
• Data owners benefit from single encryption, reducing computational and communication overhead.
• The system avoids secret key leakage risks associated with direct multi-user application of existing methods.

Conclusions:

• The developed graph intersection scheme is secure and practical for cloud-assisted IoT.
• Proxy re-encryption effectively enhances privacy and efficiency in multi-user graph computations.
• This research contributes to the advancement of privacy-preserving techniques in IoT ecosystems.