In the smart grid, efficient power supplies require near-real-time users’ electricity usage metering data, but these data might leak users’ private information, e.g., living habits. To address this problem, a number of privacy-preserving data aggregation schemes have been proposed in the literature. In this paper, we present a new type of attack, called malicious data mining attack, by which the adversary can infer a target user’s electricity usage data. When considering this attack, the majority of existing data aggregation schemes have one of the following two shortcomings. In one aspect, the schemes based on homomorphic encryption can output an accurate aggregation result, but most of them are vulnerable to this attack. In another aspect, the schemes based on differential privacy able to withstand this attack, but the random noises introduced prevent accurate aggregation results from being computed. In this paper, we propose a novel data aggregation scheme that is not only secure against the malicious data mining attack, but also capable of outputting an accurate aggregation result. Detailed security analyses indicate that the proposed scheme satisfies the desirable properties for privacy-preserving data aggregation in the smart grid, and the simulated results demonstrate that our proposed scheme enjoys low computation and communication overhead.

在智能电网中，高效的电源需要近实时的用户用电计量数据，但是这些数据可能会泄漏用户的私人信息，例如生活习惯。为了解决这个问题，在文献中已经提出了许多保护隐私的数据聚合方案。在本文中，我们提出了一种新型的攻击，称为恶意数据挖掘攻击，攻击者可借此推断目标用户的用电量数据。考虑这种攻击时，大多数现有的数据聚合方案都具有以下两个缺点之一。一方面，基于同态加密的方案可以输出准确的聚合结果，但是大多数方案都容易受到这种攻击。在另一方面，基于差分隐私的方案能够承受这种攻击，但是引入的随机噪声阻止了精确聚合结果的计算。在本文中，我们提出了一种新颖的数据聚合方案，该方案不仅可以抵御恶意数据挖掘攻击，而且还可以输出准确的聚合结果。