A Privacy-Preserving Framework Using Homomorphic Encryption for Smart Metering Systems.

Area of Science:

• Data Privacy
• Cybersecurity
• Applied Cryptography

Background:

• Smart metering systems (SMSs) collect granular consumption data, posing privacy risks through behavior recognition and absence detection.
• Homomorphic encryption (HE) offers a solution for secure computation on encrypted data, crucial for privacy-preserving data analysis.
• Existing HE solutions lack adaptability to diverse SMS application scenarios and trust boundary considerations.

Purpose of the Study:

• To propose a novel privacy-preserving framework for smart metering systems (SMSs).
• To integrate homomorphic encryption (HE) with the concept of trust boundaries for enhanced data protection.
• To demonstrate the framework's feasibility and performance across various SMS scenarios.

Main Methods:

• Developed a privacy-preserving framework implementing homomorphic encryption (HE) tailored to different SMS trust boundary scenarios.
• Evaluated the framework's performance using summation and variance computations on encrypted data.
• Set security parameters to achieve a 128-bit security level.

Main Results:

• The proposed HE framework successfully protects customer privacy in SMS data across varying trust boundaries.
• Performance evaluation showed computation times of 58,235 ms for summation and 127,423 ms for variance with 100 households.
• The computational overhead was deemed acceptable within a cost-benefit analysis for privacy assurance.

Conclusions:

• The HE-based framework provides a viable and systematic solution for privacy protection in smart metering systems.
• The trust boundary concept effectively guides the design of adaptable privacy solutions for diverse SMS applications.
• The balance between robust privacy protection and acceptable computational cost is achievable for practical SMS deployments.