Privacy Preserving and Efficient Data Collection Scheme for AMI Networks Using Deep Learning

In advanced metering infrastructure, smart meters (SMs) send fine-grained power consumption readings periodically to the utility for load monitoring and energy management. Change and transmit (CAT) is an efficient approach to collect these readings, where the readings are not transmitted when there is no enough change in consumption. However, this approach causes a privacy problem, that is, by analyzing the transmission pattern of an SM, sensitive information on the house dwellers can be inferred. For instance, since the transmission pattern is distinguishable when dwellers are on travel, attackers may analyze the pattern to launch a presence-privacy attack (PPA) to infer whether the dwellers are absent from home. In this article, we propose a scheme, called "STDL," for efficient collection of power consumption readings in advanced metering infrastructure (AMI) networks while preserving the consumers' privacy by sending spoofing transmissions using a deep-learning approach. We first use a clustering technique and real power consumption readings to create a data set for transmission patterns using the CAT approach. Then, we train a deep-learning-based attacker model, and our evaluations indicate that the attacker's success rate is about 91%. Finally, we train a deep-learning-based defense model to send spoofing transmissions efficiently to thwart the PPA. Extensive evaluations are conducted, and the results indicate that our scheme can reduce the attacker's success rate to 3.15%, while still achieving high efficiency in terms of the number of readings that should be transmitted. Our measurements indicate that the proposed scheme can increase efficiency by about 41% compared to continuously transmitting readings.