DDPFT: Secure data aggregation scheme with differential privacy and fault tolerance
Haiyong Bao, Rongxing Lu · 2015
Privacy-preserving data aggregation has been widely researched to meet the requirement of timely monitoring electricity consumption of users while protecting individual's data privacy in smart grid communications. In this paper, we propose a new secure data aggregation scheme, named DDPFT, for achieving differential privacy and fault tolerance simultaneously. Specifically, by introducing auxiliary ciphertexts subtly, a novel distributed approach for fault tolerance of data aggregation is put forward to be able to aggregate the functioning smart meter measurements flexibly and efficiently. Furthermore, DDPFT also achieves a good trade-off of accuracy and security of differential privacy for arbitrary number of malfunctioning smart meters. Moreover, through decentralizing the computational overhead and the power of the hub-like entity of the gateway, the security of our proposed scheme is enhanced and the efficiency is improved significantly. Extensive performance evaluations are conducted to illustrate that DDPFT outperforms the state-of-the-art data aggregation schemes in terms of computation complexity, communication cost, robustness of fault tolerance, and utility of differential privacy.