# Privacy-Preserving Overgrid: Secure Data Collection for the Smart Grid
## 1 Introduction  
Today, Internet of Things (IoT) scenarios where widespread technology is constantly monitoring user activities is crucial to develop algorithms for data collection, monitoring, and control that are guaranteeing the user privacy. All data regarding geolocation, power consumption, visited websites, music, and movie downloads can undoubtedly reveal sensitive information about users' habits. Exploiting the IoT paradigm, a smart grid is an innovative energy distribution network able to improve the conventional electrical grid so to be more reliable, cooperative, responsive, and economical. The smart grid technology improves two-way communications between utility companies and customers, and exploits near real-time data to make cost-effective and environment-friendly decisions. In the smart grid scenario, traditional and renewable sources are integrated and require an increment of the power system flexibility. Therefore, the control of location-dependent power demand has been tackled with demand response (DR) approaches. DR refers to the possibility of end users to change their consumption patterns in response to a dynamic price signal, or (called load control) to the possibility of directly switching or tuning specific user appliances off during peak demand.  
In this paper, we consider DR schemes for a community of smart buildings, aiming at preserving the privacy related to the single user power consumption. The key idea is to extend Overgrid [1; 2], a Peer-to-Peer (P2P)-based control scheme for distributed DR, to control a community of users with power consumption comparable to industrial utilities, but without dealing directly with thesingle residential user (whose contribution in terms of load reduction is limited anyway). Instead, we introduce Privacy-Preserving Overgrid (_PP-Overgrid_) which aims at controlling a group of smart buildings as a whole, protecting the privacy of the users through an innovative Secure Multi-Party Computation mechanism.  
We assume that such clusters of buildings are equipped with Internet connectivity and a local communication network to control the electric smart appliances and sensors. We refer to the community of buildings participating to the load control program as an Overgrid. In the present paper, we extend previous results on Overgrid in [1, 2] by introducing a mechanism that limits the shared knowledge about user habits to preserve their privacy. The main contributions of the paper are:  
* We design PP-Overgrid, a fully distributed P2P architecture able to aggregate and monitor the power consumption of the nodes, while respecting the privacy of the users. The architecture is based on a combination of P2P aggregation algorithm and secure multi-party computation.
* We analyzed the energy profiles of hundreds of real buildings, to distinguish \"flexible\" energy consumption, useful to be exploited for DR programs.
* Considering real data traces of renewable energy sources, we validate the PP-Overgrid algorithm in simulation for a large network of smart buildings, applying a statistical control of the loads.  
Hereafter, we report some Background and Related Work regarding Distribute Load Control, Overlay Networks, and Privacy Preserving schemes. In Section 3, the Overgrid architecture and the P2P protocol are described, together with an aggregation technique. In Section 4, the privacy-preserving generalization is presented and analyzed and finally some experiments and simulations studies are reported in Section 5.