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Register a new userPrivacy-Cost Management in Smart Meters Using Deep Reinforcement Learning
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Added by
Mohammadhadi Shateri
Publication date
2020
fields
Electronic Engineering
and research's language is
English
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Smart meters (SMs) play a pivotal rule in the smart grid by being able to report the electricity usage of consumers to the utility provider (UP) almost in real-time. However, this could leak sensitive information about the consumers to the UP or a third-party. Recent works have leveraged the availability of energy storage devices, e.g., a rechargeable battery (RB), in order to provide privacy to the consumers with minimal additional energy cost. In this paper, a privacy-cost management unit (PCMU) is proposed based on a model-free deep reinforcement learning algorithm, called deep double Q-learning (DDQL). Empirical results evaluated on actual SMs data are presented to compare DDQL with the state-of-the-art, i.e., classical Q-learning (CQL). Additionally, the performance of the method is investigated for two concrete cases where attackers aim to infer the actual demand load and the occupancy status of dwellings. Finally, an abstract information-theoretic characterization is provided.
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Smart meters (SMs) share fine-grained electricity consumption of households with utility providers almost in real-time. This can violate the users privacy since sensitive information is leaked through the SMs data. In this study, a novel privacy-aware method which exploits the availability of a rechargeable battery (RB) is proposed. It is based on a Markov decision process (MDP) formulation in which the reward received by the agent is designed to control the trade-off between privacy and electricity cost. To obtain a robust and general privacy measure, we adopt the mutual information (MI) between the users demand load and the masked load seen by the grid. Unlike previous studies, we model the whole temporal correlation in the data to estimate the MI in its general form. The training of the agent is done using a model-free deep reinforcement learning algorithm known as the deep double Q-learning (DDQL) method. In order to estimate the MI-based privacy signal, a neural network termed the H-network is included in the scheme. The performance of the DDQL-MI algorithm is assessed empirically using actual SMs data and compared with simpler privacy measures. The results show significant improvements over the state-of-the-art privacy-aware SMs methods.
Fine-grained Smart Meters (SMs) data recording and communication has enabled several features of Smart Grids (SGs) such as power quality monitoring, load forecasting, fault detection, and so on. In addition, it has benefited the users by giving them more control over their electricity consumption. However, it is well-known that it also discloses sensitive information about the users, i.e., an attacker can infer users private information by analyzing the SMs data. In this study, we propose a privacy-preserving approach based on non-uniform down-sampling of SMs data. We formulate this as the problem of learning a sparse representation of SMs data with minimum information leakage and maximum utility. The architecture is composed of a releaser, which is a recurrent neural network (RNN), that is trained to generate the sparse representation by masking the SMs data, and an utility and adversary networks (also RNNs), which help the releaser to minimize the leakage of information about the private attribute, while keeping the reconstruction error of the SMs data minimum (i.e., maximum utility). The performance of the proposed technique is assessed based on actual SMs data and compared with uniform down-sampling, random (non-uniform) down-sampling, as well as the state-of-the-art in privacy-preserving methods using a data manipulation approach. It is shown that our method performs better in terms of the privacy-utility trade-off while releasing much less data, thus also being more efficient.
Smart Meters (SMs) are a fundamental component of smart grids, but they carry sensitive information about users such as occupancy status of houses and therefore, they have raised serious concerns about leakage of consumers private information. In particular, we focus on real-time privacy threats, i.e., potential attackers that try to infer sensitive data from SMs reported data in an online fashion. We adopt an information-theoretic privacy measure and show that it effectively limits the performance of any real-time attacker. Using this privacy measure, we propose a general formulation to design a privatization mechanism that can provide a target level of privacy by adding a minimal amount of distortion to the SMs measurements. On the other hand, to cope with different applications, a flexible distortion measure is considered. This formulation leads to a general loss function, which is optimized using a deep learning adversarial framework, where two neural networks $-$ referred to as the releaser and the adversary $-$ are trained with opposite goals. An exhaustive empirical study is then performed to validate the performances of the proposed approach for the occupancy detection privacy problem, assuming the attacker disposes of either limited or full access to the training dataset.
Detecting inaccurate smart meters and targeting them for replacement can save significant resources. For this purpose, a novel deep-learning method was developed based on long short-term memory (LSTM) and a modified convolutional neural network (CNN) to predict electricity usage trajectories based on historical data. From the significant difference between the predicted trajectory and the observed one, the meters that cannot measure electricity accurately are located. In a case study, a proof of principle was demonstrated in detecting inaccurate meters with high accuracy for practical usage to prevent unnecessary replacement and increase the service life span of smart meters.
With the advent of 5G and the research into beyond 5G (B5G) networks, a novel and very relevant research issue is how to manage the coexistence of different types of traffic, each with very stringent but completely different requirements. In this paper we propose a deep reinforcement learning (DRL) algorithm to slice the available physical layer resources between ultra-reliable low-latency communications (URLLC) and enhanced Mobile BroadBand (eMBB) traffic. Specifically, in our setting the time-frequency resource grid is fully occupied by eMBB traffic and we train the DRL agent to employ proximal policy optimization (PPO), a state-of-the-art DRL algorithm, to dynamically allocate the incoming URLLC traffic by puncturing eMBB codewords. Assuming that each eMBB codeword can tolerate a certain limited amount of puncturing beyond which is in outage, we show that the policy devised by the DRL agent never violates the latency requirement of URLLC traffic and, at the same time, manages to keep the number of eMBB codewords in outage at minimum levels, when compared to other state-of-the-art schemes.
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