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In advanced metering infrastructure (AMI), smart meters (SMs), which areinstalled at the consumer side, send fine-grained power consumption readingsperiodically to the electricity utility for load monitoring and energymanagement. Change and transmit (CAT) is an efficient approach to collect thesereadings, where the readings are not transmitted when there is no enough changein consumption. However, this approach causes a privacy problem that is byanalyzing the transmission pattern of an SM, sensitive information on the housedwellers can be inferred. For instance, since the transmission pattern isdistinguishable when dwellers are on travel, attackers may analyze the patternto launch a presence-privacy attack (PPA) to infer whether the dwellers areabsent from home. In this paper, we propose a scheme, called "STDL", forefficient collection of power consumption readings in AMI networks whilepreserving the consumers' privacy by sending spoofing transmissions (redundantreal readings) using a deep-learning approach. We first use a clusteringtechnique and real power consumption readings to create a dataset fortransmission patterns using the CAT approach. Then, we train an attacker modelusing deep-learning, and our evaluations indicate that the success rate of theattacker is about 91%. Finally, we train a deep-learning-based defense model tosend spoofing transmissions efficiently to thwart the PPA. Extensiveevaluations are conducted, and the results indicate that our scheme can reducethe attacker's success rate, to 13.52% in case he knows the defense model andto 3.15% in case he does not know the model, while still achieving highefficiency in terms of the number of readings that should be transmitted. Ourmeasurements indicate that the proposed scheme can reduce the number ofreadings that should be transmitted by about 41% compared to continuouslytransmitting readings.

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