Efficient privacy-preserving aggregation for demand side management of residential loads

An effective tool used in practice to maintain network balance is residential demand side management (DMS). However, privacy concerns related to the processing of user personal consumption data often result in a slow wide-scale adoption and acceptance. Various general purpose cryptographic techniques are available, such as homomorphic encryption and secure multi-party computation (MPC), to address these privacy-concerns. Unfortunately, these come at a significant computation price which makes them not applicable in many practical scenarios. In this work, a privacy-preserving aggregation algorithm, based on additive random shares and a combination of well-established symmetric and asymmetric key cryptography methods is proposed and compared with additive homomorphic encryption (AHE) techniques and state-of-the-art MPC protocols. The results show that generic techniques such as homomorphic encryption come with significant computational and communication cost, especially at the users side. On the other hand, MPC approaches provide better performance and resilience to dropouts for very large networks but entail a complex communication among users. In this context, the proposed additive random shares algorithm was the most balanced choice for DSM, with a good performance, simpler information flow and the possibility of adding redundant intermediary parties for enhanced resilience.

Automated summary

Residential demand side management (DMS) is an effective tool for maintaining network balance, but privacy concerns related to user personal consumption data have hindered its widespread adoption. This paper proposes a privacy-preserving aggregation algorithm based on additive random shares and a combination of symmetric and asymmetric key cryptography methods. The algorithm is compared with other techniques such as additive homomorphic encryption (AHE) and state-of-the-art MPC protocols. The results show that while generic techniques like homomorphic encryption are computationally expensive, MPC approaches provide better performance and resilience for large networks. The proposed additive random shares algorithm is considered the most balanced choice for DSM, offering good performance, simpler information flow, and the ability to add redundant intermediary parties for enhanced resilience.