For constrained end devices in Internet of Things, such as smart meters (SMs), data transmission is an energy-consuming operation. To address this problem, we propose an efficient and privacy-preserving aggregation system with the aid of Fog computing architecture, named PPFA, which enables the intermediate Fog nodes to periodically collect data from nearby SMs and accurately derive aggregate statistics as the fine-grained Fog level aggregation. The Cloud/utility supplier computes overall aggregate statistics by aggregating Fog level aggregation. To minimize the privacy leakage and mitigate the utility loss, we use more efficient and concentrated Gaussian mechanism to distribute noise generation among parties, thus offering provable differential privacy guarantees of the aggregate statistic on both Fog level and Cloud level. In addition, to ensure aggregator obliviousness and system robustness, we put forward a two-layer encryption scheme: the first layer applies OTP to encrypt individual noisy measurement to achieve aggregator obliviousness, while the second layer uses public-key cryptography for authentication purpose. Our scheme is simple, efficient, and practical, it requires only one round of data exchange among a SM, its connected Fog node and the Cloud if there are no node failures, otherwise, one extra round is needed between a meter, its connected Fog node, and the trusted third party.

中文翻译：

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PPFA：智能电网中隐私保护的雾聚合


对于物联网中的受限终端设备，例如智能电表（SM），数据传输是一项耗能操作。为了解决这个问题，我们提出了一种借助雾计算架构的高效且保护隐私的聚合系统，称为PPFA，它使得中间Fog节点能够定期从附近的SM收集数据，并准确地得出聚合统计数据作为细粒度的Fog级别聚合。云/公用事业供应商通过聚合雾级别聚合来计算总体聚合统计数据。为了最大限度地减少隐私泄漏并减轻效用损失，我们使用更高效、更集中的高斯机制来分配各方之间的噪声生成，从而在雾级和云级上提供可证明的聚合统计差异隐私保证。此外，为了确保聚合器的健忘性和系统的鲁棒性，我们提出了一种双层加密方案：第一层应用OTP对个体噪声测量进行加密以实现聚合器的健忘性，而第二层使用公钥密码学用于身份验证目的。我们的方案简单、高效、实用，如果没有节点故障，SM及其连接的Fog节点和云端之间只需要进行一轮数据交换，否则，仪表与其连接的Fog之间需要额外进行一轮数据交换节点和可信第三方。