EPIC: Efficient Privacy-Preserving Scheme with E2E Data Integrity and Authenticity for AMI Networks

TL;DR

EPIC is a cryptographic scheme for AMI networks that ensures privacy, data integrity, and authenticity of power consumption readings while being efficient and resistant to collusion attacks.

Contribution

EPIC introduces an efficient cryptographic protocol for privacy-preserving data collection with end-to-end integrity verification in AMI networks, addressing previous schemes' limitations.

Findings

EPIC reduces computational and communication overhead compared to existing schemes.

EPIC effectively verifies data integrity without exposing individual readings.

EPIC resists collusion attacks between utility and relay nodes.

Abstract

In Advanced Metering Infrastructure (AMI) networks, smart meters should send fine-grained power consumption readings to electric utilities to perform real-time monitoring and energy management. However, these readings can leak sensitive information about consumers' activities. Various privacy-preserving schemes for collecting fine-grained readings have been proposed for AMI networks. These schemes aggregate individual readings and send an aggregated reading to the utility, but they extensively use asymmetric-key cryptography which involves large computation/communication overhead. Furthermore, they do not address End-to-End (E2E) data integrity, authenticity, and computing electricity bills based on dynamic prices. In this paper, we propose EPIC, an efficient and privacy-preserving data collection scheme with E2E data integrity verification for AMI networks. Using efficient cryptographic operations, each meter should send a masked reading to the utility such that all the masks are canceled after aggregating all meters' masked readings, and thus the utility can only obtain an aggregated reading to preserve consumers' privacy. The utility can verify the aggregated reading integrity without accessing the individual readings to preserve privacy. It can also identify the attackers and compute electricity bills efficiently by using the fine-grained readings without violating privacy. Furthermore, EPIC can resist collusion attacks in which the utility colludes with a relay node to extract the meters' readings. A formal proof, probabilistic analysis are used to evaluate the security of EPIC, and ns-3 is used to implement EPIC and evaluate the network performance. In addition, we compare EPIC to existing data collection schemes in terms of overhead and security/privacy features.