Countering Presence Privacy Attack in Efficient AMI Networks Using Interactive Deep-Learning

Reporting fine-grained power consumption readings periodically in advanced metering infrastructure (AMI) results in transmitting a massive amount of data by each smart meter (SM). To collect these readings efficiently, change and transmit (CAT) approach can be used. In CAT, the SM sends a consumption reading only when there is enough change in the consumption, which reduces the number of transmitted readings. However, using the CAT approach may trigger attackers to launch a presence-privacy attack (PPA) to infer sensitive information such as the absence of the house occupants by analyzing their SM's transmission pattern. Therefore, in this paper, we propose a scheme, called 'STID', for collecting the power consumption readings efficiently in AMI networks while preserving the consumers' privacy by transmitting spoofing transmissions based on an interactive deep-learning defense model. First, we create a dataset that contains the CAT transmission patterns using real power consumption readings and a clustering technique. Next, we train a deep-learning-based attacker model to launch PPA, and the results indicate that the success rate of the attacker is about 90%. Finally, to mitigate the PPA, we train a defense model using deep-learning to transmit spoofing transmissions. The evaluations of our envisioned STID scheme demonstrate a significant reduction in the attacker's success rate while achieving high efficiency in terms of the number of readings that should be transmitted. Our measurements indicate that our proposed STID can reduce the attacker's success rate to 6.12% and increase efficiency by about 38% compared to transmitting readings periodically.