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Communication Dans Un Congrès Année : 2020

On the Impact of Side Information on Smart Meter Privacy-Preserving Methods

Résumé

Smart meters (SMs) can pose privacy threats for consumers, an issue that has received significant attention in recent years. This paper studies the impact of Side Information (SI) on the performance of possible attacks to real-time privacy-preserving algorithms for SMs. In particular, we consider a deep adversarial learning framework, in which the desired releaser, which is a Recurrent Neural Network (RNN), is trained by fighting against an adversary network until convergence. To define the objective for training, two different approaches are considered: the Causal Adversarial Learning (CAL) and the Directed Information (DI)based learning. The main difference between these approaches relies on how the privacy term is measured during the training process. The releaser in the CAL method, disposing of supervision from the actual values of the private variables and feedback from the adversary performance, tries to minimize the adversary loglikelihood. On the other hand, the releaser in the DI approach completely relies on the feedback received from the adversary and is optimized to maximize its uncertainty. The performance of these two algorithms is evaluated empirically using real-world SMs data, considering an attacker with access to SI (e.g., the day of the week) that tries to infer the occupancy status from the released SMs data. The results show that, although they perform similarly when the attacker does not exploit the SI, in general, the CAL method is less sensitive to the inclusion of SI. However, in both cases, privacy levels are significantly affected, particularly when multiple sources of SI are included.
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Dates et versions

hal-04137018 , version 1 (22-06-2023)

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Citer

Mohammadhadi Shateri, Francisco Messina, Pablo Piantanida, Fabrice Labeau. On the Impact of Side Information on Smart Meter Privacy-Preserving Methods. 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), Nov 2020, Tempe, Italy. pp.1-6, ⟨10.1109/smartgridcomm47815.2020.9302948⟩. ⟨hal-04137018⟩
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