Privacy preserving IoT-based crowd-sensing network with comparable homomorphic encryption and its application in...

Area of Science:

• Cryptography and Network Security
• Internet of Things (IoT)
• Data Privacy

Background:

• IoT-based crowd-sensing networks are increasingly popular for data collection but face privacy risks due to sensitive data transmission.
• Homomorphic encryption (HE) enables computations on encrypted data, making privacy-preserving crowd-sensing feasible.
• Efficient ciphertext comparison is crucial for secure data processing in HE-based schemes but is often lacking.

Purpose of the Study:

• To propose novel comparable homomorphic encryption (CompHE) schemes for IoT crowd-sensing networks.
• To enable efficient and secure ciphertext comparison among multiple users.
• To demonstrate practical applications in public health, such as COVID-19 contact tracing and social distancing.

Main Methods:

• Development of CompHE schemes utilizing Lagrange's interpolation technique.
• Security proofs for the proposed schemes.
• Performance analysis to evaluate practicality and efficiency.

Main Results:

• The proposed CompHE schemes enable efficient ciphertext comparison within IoT crowd-sensing networks.
• Security analysis confirms the schemes' robustness, offering collusion resistance or security in the semi-honest model.
• Performance evaluation shows significant improvements over existing schemes, requiring fewer modular exponentiations.

Conclusions:

• The developed CompHE schemes provide a practical and secure solution for privacy-preserving data aggregation and comparison in IoT crowd-sensing.
• These schemes are applicable to critical public health scenarios like contact tracing and social distancing, mitigating privacy concerns.
• The research advances the field by offering more secure and efficient comparable homomorphic encryption methods.