With the emergence of cyber-physical systems (CPSs) in utility systems like electricity, water, and gas networks, data collection has become more prevalent. While data collection in these systems has numerous advantages, it also raises concerns about privacy as it can potentially reveal sensitive information about users. To address this issue, we propose a Bayesian approach to control the adversarial inference and mitigate the physical-layer privacy problem in CPSs. Specifically, we develop a control strategy for the worst-case scenario where an adversary has perfect knowledge of the user’s control strategy. For finite state-space problems, we derive the fixed-point Bellman’s equation for an optimal stationary strategy and discuss a few practical approaches to solve it using optimization-based control design. Addressing the computational complexity, we propose a reinforcement learning approach based on the Actor-Critic architecture. To also support smart meter privacy research, we present a publicly accessible “Co-LivEn” dataset with comprehensive electrical measurements of appliances in a co-living household. Using this dataset, we benchmark the proposed reinforcement learning approach. The results demonstrate its effectiveness in reducing privacy leakage. Our work provides valuable insights and practical solutions for managing adversarial inference in cyber-physical systems, with a particular focus on enhancing privacy in smart meter applications.

Smart meters, now an essential component of modern power grids, allow energy providers to remotely monitor users' energy consumption in near real-time. While this technology offers numerous advantages for energy management and system efficiency, it also poses significant privacy concerns. High-resolution energy consumption data can reveal sensitive information about users' routines and activities, thus potentially jeopardizing their privacy. In particular, research has demonstrated that Bayesian inference attacks can effectively disaggregate smart meter data to deduce household appliance states and subsequently obtain sensitive user information.

This thesis investigates the use of energy storage systems to protect smart meter data privacy against Bayesian inference attacks. Although several methods have been proposed in the literature that employ energy storage systems for this purpose, many rely on ideal assumptions such as lossless energy storage systems. To address this issue, a data-driven energy storage model that considers energy losses and capacity degradation has been proposed. Privacy leakage is quantified in terms of Bayesian risk, and control strategies are devised to minimize Bayesian risk while accounting for the energy storage system's operational constraints and economic implications. The findings reveal that non-idealities in energy storage systems significantly affect the privacy-preserving performance of control strategies. Moreover, incorporating degradation losses in the design of privacy-enhancing control strategies considerably improves battery life, albeit with some privacy loss.

Taking into account the non-idealities of energy storage, this thesis introduces novel privacy-preserving control strategies using various adversarial models, which are classified based on their knowledge of the control system. These models include controller-aware and controller-unaware adversaries employing sequential hypothesis testing or maximum a posteriori detection. The proposed control strategies are evaluated through numerical simulations using real data and emulated energy storage systems. Additionally, the thesis provides a reference dataset of appliance power consumption, featuring detailed electrical measurements to support future smart meter privacy research. In summary, this work offers valuable insights and practical solutions for managing adversarial inference in cyber-physical systems, with potential applications extending to other sensor networks beyond smart meters.

Smarta elmätare, idag en väsentlig komponent i moderna elnät, gör att elleverantörer kan fjärrövervaka användarnas energiförbrukning i nästan realtid. Även om denna teknik erbjuder många fördelar för energihantering och systemeffektivitet, utgör den också ett betydande integritetsproblem. Högupplösta energiförbrukningsdata kan avslöja känslig information om användarnas rutiner och aktiviteter och därmed potentiellt äventyra deras integritet. Speciellt har forskning visat att Bayesianska slutledningsattacker effektivt kan disaggregera data från smarta elmätare för att härleda hushållsapparaters tillstånd och därefter erhålla känslig användarinformation.

Denna avhandling undersöker användningen av energilagringssystem för att skydda smarta elmätares dataintegritet mot Bayesianska slutledningsattacker. Även om flera metoder som använder energilagringssystem för detta ändamål har föreslagits i litteraturen, förlitar sig många på idealiska antaganden såsom förlustfria energilagringssystem. För att lösa detta problem har en datadriven energilagringsmodell som tar hänsyn till energiförluster och kapacitetsförsämringar föreslagits. Sekretessläckage kvantifieras i termer av Bayesiansk risk, och kontrollstrategier utformas för att minimera detta samtidigt som man tar hänsyn till energilagringssystemets operativa begränsningar och ekonomiska konsekvenser. Våra resultat visar att icke-idealiteter i energilagringssystem avsevärt påverkar kontrollstrategiernas integritetsbevarande prestanda. Dessutom förbättras batteriets livslängd avsevärt om  försämringsför-luster i utformningen av integritetsförbättrande kontrollstrategier inkluderas, om än med viss integritetsförlust.

Med hänsyn till icke-ideala energilager, introducerar denna avhandling nya kontrollstrategier som bevarar integriteten med hjälp av olika kontradiktoriska modeller, som klassificeras baserat på deras kunskap om kontrollsystemet. Dessa modeller inkluderar kontroller-medvetna och kontroller-omedvetna mot-ståndare som använder sekventiell hypotestestning eller maximal a posteriori-detektion. De föreslagna styrstrategierna utvärderas genom numeriska simuleringar med  riktiga mätdata och emulerade energilagringssystem. Dessutom tillhandahåller avhandlingen ett referensdataset  apparatens strömförbrukning, med detaljerade elektriska mätningar för att stödja framtida smarta elmätares integritetsforskning. Sammanfattningsvis erbjuder detta arbete värdefulla insikter och praktiska lösningar för att hantera kontradiktoriska slutsatser i cyberfysiska system, med potentiella tillämpningar som sträcker sig till andra sensornätverk bortom smarta elmätare.

Non-intrusive load monitoring (NILM) is the process of disaggregating total electricity consumption measured by a smart meter into individual appliances’ contributions. In this paper, we present a privacy control strategy that selectively filters appliances’ consumption from the smart meter measurements to hinder NILM disaggregation performance. The privacy controller uses charging and discharging operations of an energy storage to achieve desired smart meter measurements. We model the household consumption using both additive and difference factorial hidden Markov models and design a control strategy to minimize privacy leakage measured in terms of Bayesian risk due to maximum a posteriori detection. Due to the high computational complexity of the optimal control strategy, we propose a computationally efficient sub-optimal strategy. We evaluate the proposed approaches using the ECO data set and show their privacy improvements against the Viterbi disaggregation algorithm.

Demand-side management (DSM) is a process by which the user demand patterns are modified to meet certain desired objectives. Traditionally, DSM was utility-driven, but with an increase in the integration of renewable sources and privacy-conscious consumers, it also becomes a “consumer-driven" process. Promising theoretical studies have shown that privacy can be achieved by shaping the user demand using an energy storage system (ESS). In this paper, we present a framework for utility-driven DSM while considering the user privacy and the ESS operational cost due to its energy losses and capacity degradation. We propose an ESS model using a circuit-based and data-driven approach that can be used to capture the ESS characteristics in control strategy designs. We measure privacy leakage using the Bayesian risk of a hypothesis testing adversary and present a novel recursive algorithm to compute the optimal privacy control strategy. Further, we design an energy-flow control strategy that achieves the Pareto-optimal trade-off between privacy leakage, deviation of demand from a DSM target profile, and the ESS cost. With numerical experiments using real household data and an emulated lithium-ion battery, we show that the desired level of privacy and demand shaping performance can be achieved while reducing the ESS degradation.

There is a growing interest in analysing freshness of data in networked systems. Age of Information (AoI) has emerged as a relevant metric to quantify this freshness at a receiver, and minimizing this metric for different system models has received significant research attention. However, a fundamental question remains: what is the minimum achievable AoI in any single-server-single-source queuing system for a given service-time distribution? We address this question for the average peak AoI (PAoI) statistic by considering generate-at-will source model, service preemptions, and request delays. Our main result is on the characterization of the minimum achievable average PAoI, and we show that it is achieved by a fixed-threshold policy among the set of all causal policies. We use the characterization to provide necessary and sufficient condition for preemptions to be beneficial for a given service-time distribution. Our numerical results, obtained using well-known distributions, demonstrate that the heavier the tail of a distribution the higher the performance gains of using preemptions.

We study the optimal control problem of the maximum a posteriori (MAP) state sequence detection of an adversary using smart meter data. The privacy leakage is measured using the Bayesian risk and the privacy-enhancing control is achieved in real-time using an energy storage system. The control strategy is designed to minimize the expected performance of a non-causal adversary at each time instant. With a discrete-state Markov model, we study two detection problems: when the adversary is unaware or aware of the control. We show that the adversary in the former case can be controlled optimally. In the latter case, where the optimal control problem is shown to be non-convex, we propose an adaptive-grid approximation algorithm to obtain a sub-optimal strategy with reduced complexity. Although this work focuses on privacy in smart meters, it can be generalized to other sensor networks. 

There is a growing interest in analysing the freshness of data in networked systems. Age of Information (AoI) has emerged as a popular metric to quantify this freshness at a given destination. There has been a significant research effort in optimizing this metric in communication and networking systems under different settings. In contrast to previous works, we are interested in a fundamental question, what is the minimum achievable AoI in any single-server-single-source queuing system for a given service-time distribution? To address this question, we study a problem of optimizing AoI under service preemptions. Our main result is on the characterization of the minimum achievable average peak AoI (PAoI). We obtain this result by showing that a fixed-threshold policy is optimal in the set of all randomized-threshold causal policies. We use the characterization to provide necessary and sufficient conditions for the service-time distributions under which preemptions are beneficial.

In this work, we propose an optimal privacy-by-design strategy using an energy storage system (ESS) that is capable of shaping the user demand to follow a time-varying target profile. In addition, we consider the ESS usage cost due to its energy losses and capacity degradation. We measure the privacy leakage in terms of the Bayesian risk. The proposed strategy is computed by solving a multi-objective optimization problem using the Markov decision process framework. With numerical simulations using real household consumption data and a lithium-ion battery model, we study the trade-off between the achievable Bayesian risk, the variations in the user demand from the target profile and the energy storage cost. The results show that by trading-off some privacy, the variations in the user demand can be reduced while improving the battery lifetime.

In this paper, we present a degradation-aware privacy control strategy for smart meters by taking into account the capacity fade and energy loss of the battery, which has not been included previously. The energy management strategy is designed by minimizing the weighted sum of both privacy loss and total energy storage losses, where the weightage is set using a trade-off parameter. The privacy loss is measured in terms of Bayesian risk of an unauthorized hypothesis test. By making first-order Markov assumptions, the stochastic parameters of energy loss and capacity fade of the energy storage system are modelled using degradation maps. Using household power consumption data from the ECO dataset, the proposed control strategy is numerically evaluated for different trade-off parameters. Results show that, by including the degradation losses in the design of the privacy-enhancing control strategy, significant improvement in battery life can be achieved, in general, at the expense of some privacy loss.

Privacy-preserving smart meter control strategies proposed in the literature so far make some ideal assumptions such as instantaneous control without delay, lossless energy storage systems etc. In this paper, we present a one-step-ahead predictive control strategy using Bayesian risk to measure and control privacy leakage with an energy storage system. The controller estimates energy state using a three-circuit energy storage model to account for steady-state energy losses. With numerical experiments, the controller is evaluated with real household consumption data using a state-of-the-art adversarial algorithm. Results show that the state estimation of the energy storage system significantly affects the controller's performance. The results also show that the privacy leakage can be effectively reduced using an energy storage system but at the expense of energy loss.