  In advanced metering infrastructure (AMI), smart meters (SMs), which are
installed at the consumer side, send fine-grained power consumption readings
periodically to the electricity 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 paper, we propose a scheme, called "STDL", for
efficient collection of power consumption readings in AMI networks while
preserving the consumers' privacy by sending spoofing transmissions (redundant
real readings) using a deep-learning approach. We first use a clustering
technique and real power consumption readings to create a dataset for
transmission patterns using the CAT approach. Then, we train an attacker model
using deep-learning, and our evaluations indicate that the success rate of the
attacker 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 13.52% in case he knows the defense model and
to 3.15% in case he does not know the model, while still achieving high
efficiency in terms of the number of readings that should be transmitted. Our
measurements indicate that the proposed scheme can reduce the number of
readings that should be transmitted by about 41% compared to continuously
transmitting readings.
