Biblio

Smart Meters (SM) are IoT end devices used to collect user utility consumption with limited processing power on the edge of the smart grid (SG). While SMs have great applications in providing data analysis to the utility provider and consumers, private user information can be inferred from SMs readings. For preserving user privacy, a number of methods were developed that use perturbation by adding noise to alter user load and hide consumer data. Most methods limit the amount of perturbation noise using differential privacy to preserve the benefits of data analysis. However, additive noise perturbation may have an undesirable effect on billing. Additionally, users may desire to select complete privacy without giving consent to having their data analyzed. We present a virtual battery model that uses perturbation with additive noise obtained from a virtual chargeable battery. The level of noise can be set to make user data differentially private preserving statistics or break differential privacy discarding the benefits of data analysis for more privacy. Our model uses fog aggregation with authentication and encryption that employs lightweight cryptographic primitives. We use Diffie-Hellman key exchange for symmetrical encryption of transferred data and a two-way challenge-response method for authentication.

A major challenge for utilities is energy theft, wherein malicious actors steal energy for financial gain. One such form of theft in the smart grid is the fraudulent amplification of energy generation measurements from DERs, such as photo-voltaics. It is important to detect this form of malicious activity, but in a way that ensures the privacy of customers. Not considering privacy aspects could result in a backlash from customers and a heavily curtailed deployment of services, for example. In this short paper, we present a novel privacy-preserving approach to the detection of manipulated DER generation measurements.

The smart grid changes the way energy is produced and distributed. In addition both, energy and information is exchanged bidirectionally among participating parties. Therefore heterogeneous systems have to cooperate effectively in order to achieve a common high-level use case, such as smart metering for billing or demand response for load curtailment. Furthermore, a substantial amount of personal data is often needed for achieving that goal. Capturing and processing personal data in the smart grid increases customer concerns about privacy and in addition, certain statutory and operational requirements regarding privacy aware data processing and storage have to be met. An increase of privacy constraints, however, often limits the operational capabilities of the system. In this paper, we present an approach that automates the process of finding an optimal balance between privacy requirements and operational requirements in a smart grid use case and application scenario. This is achieved by formally describing use cases in an abstract model and by finding an algorithm that determines the optimum balance by forward mapping privacy and operational impacts. For this optimal balancing algorithm both, a numeric approximation and - if feasible - an analytic assessment are presented and investigated. The system is evaluated by applying the tool to a real-world use case from the University of Southern California (USC) microgrid.

An advanced metering infrastructure (AMI) allows real-time fine-grained monitoring of the energy consumption data of individual consumers. Collected metering data can be used for a multitude of applications. For example, energy demand forecasting, based on the reported fine-grained consumption, can help manage the near future energy production. However, fine- grained metering data reporting can lead to privacy concerns. It is, therefore, imperative that the utility company receives the fine-grained data needed to perform the intended demand response service, without learning any sensitive information about individual consumers. In this paper, we propose an anonymous privacy preserving fine-grained data aggregation scheme for AMI networks. In this scheme, the utility company receives only the distribution of the energy consumption by the consumers at different time slots. We leverage a network tree topology structure in which each smart meter randomly reports its energy consumption data to its parent smart meter (according to the tree). The parent node updates the consumption distribution and forwards the data to the utility company. Our analysis results show that the proposed scheme can preserve the privacy and security of individual consumers while guaranteeing the demand response service.

Data security in smart metering applications is important not only to secure the customer privacy but also to protect the power utility against fraud attempts. Usual deployment of metering applications rely on the power utility infrastructure, assuming its Advanced Metering Infrastructure (AMI) as trustworthy. This paper describes the design and deployment of a smart metering system focusing on the security of the AMI (smart meters, data aggregator on the field, Metering Data Collection system and metering database) considering the data processing on untrusted clouds. We discuss one use case of the SecureCloud project, an ongoing project that investigates how security and privacy requirements of smart grid applications can be met with a secure cloud platform based on Intel SGX enclaves. The paper describes the components of the advanced metering system as well as the security approach adopted to meet its requirements. A smart metering application has been prototyped in the SecureCloud platform and the integration challenges are discussed from the perspectives of security, privacy and scalability.

Smart meters provide fine-grained electricity consumption reporting to electricity providers. This constitutes an invasive factor into the privacy of the consumers, which has raised many privacy concerns. Although billing requires attributable consumption reporting, consumption reporting for operational monitoring and control measures can be non-attributable. However, the privacy-preserving AMS schemes in the literature tend to address these two categories disjointly — possibly due to their somewhat contradictory characteristics. In this paper, we propose an efficient two-party privacy-preserving cryptographic scheme that addresses operational control measures and billing jointly. It is computationally efficient as it is based on symmetric cryptographic primitives. No online trusted third party (TTP) is required.

This paper proposes a novel privacy-preserving smart metering system for aggregating distributed smart meter data. It addresses two important challenges: (i) individual users wish to publish sensitive smart metering data for specific purposes, and (ii) an untrusted aggregator aims to make queries on the aggregate data. We handle these challenges using two main techniques. First, we propose Fourier Perturbation Algorithm (FPA) and Wavelet Perturbation Algorithm (WPA) which utilize Fourier/Wavelet transformation and distributed differential privacy (DDP) to provide privacy for the released statistic with provable sensitivity and error bounds. Second, we leverage an exponential ElGamal encryption mechanism to enable secure communications between the users and the untrusted aggregator. Standard differential privacy techniques perform poorly for time-series data as it results in a Θ(n) noise to answer n queries, rendering the answers practically useless if n is large. Our proposed distributed differential privacy mechanism relies on Gaussian principles to generate distributed noise, which guarantees differential privacy for each user with O(1) error, and provides computational simplicity and scalability. Compared with Gaussian Perturbation Algorithm (GPA) which adds distributed Gaussian noise to the original data, the experimental results demonstrate the superiority of the proposed FPA and WPA by adding noise to the transformed coefficients.