Smart micro-grid architectures are small scale electricity provision networks composed of individual electricity providers and consumers. Supporting micro-grids with computationally limited devices, is a cost-effective approach to service provisioning in resource-limited settings. However, the limited availability of real time measurements and the unreliable communication network makes the use of Advanced Metering Infrastructure (AMI) for monitoring and control a challenging problem. Grid operation and stability are therefore reliant on inaccurate and incomplete information. Consequently, data gathering and analytics raise privacy concerns for grid users, which is undesirable. In this paper, we study adversarial scenarios for the privacy violations on micro-grids. We consider two types of privacy threats in constrained micro-grids, namely inferential and aggregation attacks. The reason is that both attacks capture scenarios that can be used to provoke energy theft and destabilize the grid. Grid destabilzation leads to distrust between suppliers and consumers. This work provides a roadmap towards a secure and resilient smart micro-grid energy networks.
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