Privacy-preserving Smart Metering Research Articles

Private Stream Aggregation with Labels in the Standard Model

Abstract A private stream aggregation (PSA) scheme is a protocol of n clients and one aggregator. At every time step, the clients send an encrypted value to the (untrusted) aggregator, who is able to compute the sum of all client values, but cannot learn the values of individual clients. One possible application of PSA is privacy-preserving smart-metering, where a power supplier can learn the total power consumption, but not the consumption of individual households. We construct a simple PSA scheme that supports labels and which we prove to be secure in the standard model. Labels are useful to restrict the access of the aggregator, because it prevents the aggregator from combining ciphertexts with different labels (or from different time-steps) and thus avoids leaking information about values of individual clients. The scheme is based on key-homomorphic pseudorandom functions (PRFs) as the only primitive, supports a large message space, scales well for a large number of users and has small ciphertexts. We provide an implementation of the scheme with a lattice-based key-homomorphic PRF (secure in the ROM) and measure the performance of the implementation. Furthermore, we discuss practical issues such as how to avoid a trusted party during the setup and how to cope with clients joining or leaving the system.

Identity-Based Verifiable Aggregator Oblivious Encryption and Its Applications in Smart Grids

Verifiable aggregator oblivious encryption is a useful notion that not only allows an untrusted aggregator to compute aggregated data without learning any other information but also requires the aggregator to generate a proof of the aggregated data so that anyone can verify the accuracy. Since the aggregator does not learn data that may reveal users' private information and also cannot forge the aggregated data, verifiable aggregator oblivious encryption is suitable for privacy-preserving smart metering. However, NIST has recommended not using public-key-based cryptography in the smart grid because public-key cryptography cannot provide sufficient scalability. Thus, we propose a notion of identity-based verifiable aggregator oblivious encryption and design concrete schemes in this paper. We prove the aggregator oblivious security and aggregator unforgeability of our schemes via the smooth projective hash function and computational Diffie-Hellman assumption. Furthermore, based on our identity-based verifiable aggregator oblivious encryption scheme, we propose an identity-based data aggregation protocol for the smart grid and provide a security analysis in the context of seven typical attacks on smart grids. The implementation of our protocol via the Intel Edison platform shows that it is sufficiently lightweight for resource-constrained smart meters.

Privacy-Functionality Trade-Off: A Privacy-Preserving Multi-Channel Smart Metering System

While smart meters can provide households with more autonomy regarding their energy consumption, they can also be a significant intrusion into the household’s privacy. There is abundant research implementing protection methods for different aspects (e.g., noise-adding and data aggregation, data down-sampling); while the private data are protected as sensitive information is hidden, some of the compulsory functions such as Time-of-use (TOU) billing or value-added services are sacrificed. Moreover, some methods, such as rechargeable batteries and homomorphic encryption, require an expensive energy storage system or central processor with high computation ability, which is unrealistic for mass roll-out. In this paper, we propose a privacy-preserving smart metering system which is a combination of existing data aggregation and data down-sampling mechanisms. The system takes an angle based on the ethical concerns about privacy and it implements a hybrid privacy-utility trade-off strategy, without sacrificing functionality. In the proposed system, the smart meter plays the role of assistant processor rather than information sender/receiver, and it enables three communication channels to transmit different temporal resolution data to protect privacy and allow freedom of choice: high frequency feed-level/substation-level data are adopted for grid operation and management purposes, low frequency household-level data are used for billing, and a privacy-preserving valued-add service channel to provide third party (TP) services. In the end of the paper, the privacy performance is evaluated to examine whether the proposed system satisfies the privacy and functionality requirements.

Balancing Security and Efficiency for Smart Metering Against Misbehaving Collectors

Smart grid enables two-way communications between smart meters and operation centers to collect real-time power consumption of customers to improve flexibility, reliability, and efficiency of the power system. It brings serious privacy issues to customers, since the meter readings possibly expose customers’ activities in the house. Data encryption can protect the readings, but lengthens the data size. Secure data aggregation improves communication efficiency and preserves customers’ privacy, while fails to support dynamic billing, or offer integrity protection against public collectors, which may be hacked in reality. In this paper, we define a new security model to formalize the misbehavior of collectors, in which the misbehaving collectors may launch pollution attacks to corrupt power consumption data. Under this model, we propose a novel privacy-preserving smart metering scheme to prevent pollution attacks for the balance of security and efficiency in smart grid. It achieves end-to-end security, data aggregation, and integrity protection against the misbehaving collectors, which act as local gateways to collect and aggregate usage data and forward to operation centers. As a result, the misbehaving collectors cannot access or corrupt power usage data of customers. In addition, we design a dynamic billing mechanism based on individual power consumption maintained on collectors with the verification of customers. Our analysis shows that the proposed scheme achieves secure smart metering and verifiable dynamic billing against misbehaving collectors with low computational and communication overhead.

A Privacy-Preserving Distributed Smart Metering Temporal and Spatial Aggregation Scheme

A distributed temporal and spatial aggregation scheme for privacy-preserving smart metering is presented. This scheme preserves the privacy of fine-grained utility usage data of individual users while reporting: 1) spatially aggregated utility usage data of a community at fine-grained time steps (e.g., minutes) and 2) temporally aggregated utility usage data of individual users to the utility company over a billing period (e.g., a month). Each meter privately partitions the fine-grained utility data into k partitions and sends them to (homomorphically encrypted) k - 1 pairwise smart meters in the community, while keeping the remaining for itself. A smart meter will report to the aggregator, a locally aggregated usage amount of its own and of all its pairwise nodes at the fine-grained time scale allowing community-wide spatial aggregation at the aggregator without revealing individual fine-grained usage data. A smart meter will temporally aggregate usage data from each of its pairwise nodes and transmit it to the aggregator where the overall temporal aggregation will be performed by each smart meter. Our scheme not only can prevent privacy theft from external attackers but also is resilient to internal attacks by a subset of smart meters or even colluding gateway aggregator and the control center. This is a distinct advantage of our scheme over existing works without incurring any computation and communication overheads.

Privacy-Preserving Aggregation of Time-Series Data with Public Verifiability from Simple Assumptions and Its Implementations

Aggregator oblivious encryption was proposed by Shi et al. (NDSS 2011). In this method, an aggregator can compute an aggregated sum of data and is unable to learn anything else (aggregator obliviousness). Since the aggregator does not learn individual data that may reveal users’ habits and behaviors, several applications including privacy-preserving smart metering have been considered. In this paper, we propose an aggregator oblivious encryption scheme with public verifiability where the aggregator is required to generate a proof of an aggregated sum, and anyone can verify whether the aggregated sum has been correctly computed by the aggregator. Although Leontiadis et al. (CANS 2015) considered verifiability, their scheme requires an interactive complexity assumption to provide the unforgeability of the proof. Our scheme is proven to be unforgeable under a static and simple assumption (a variant of the Computational Diffie–Hellman assumption). Moreover, our scheme inherits the tightness of the reduction of the Benhamouda et al. scheme (ACM TISSEC 2016) for proving aggregator obliviousness. This tight reduction allows us to employ elliptic curves of a smaller order and leads to efficient implementation. Specifically, for 112-bit security, we can employ Barreto–Naehrig (BN) curves with a 383-bit prime order, whereas we need to employ curves with a 1031-bit prime order to implement the Leontiadis et al. scheme. We give implementations of two schemes and evaluate their performances under those curves. We employ a Raspberry-Pi as a power-constrained device such as a smart meter. Consequently, we demonstrate that the running time of the data encryption, data aggregation and verification in our scheme are reduced by approximately 74%, 64% and 89%, respectively, compared to those of the Leontiadis et al. scheme.

A New Approach for Privacy-Aware Smart Metering using the Concept of Software Defined Networking

The direct and two-way communication between energy suppliers and high-frequency smart meters enables the suppliers to implement dynamic pricing and manage demand response. Fine-grained monitoring of energy consumption also helps consumers improve their energy profile. This granularity, however, may impose a threat on household privacy. Detailed profile of energy usage can be mapped into detailed profile of daily activities. Many proposals attempted to mitigate this problem and introduce privacy-preserving smart metering. The main approach is to process meter data before they are sent to the energy provider, through de-identification, aggregation or encryption. This processing can take place locally on the smart meter itself, or through a trusted third party. Such solutions suffer from increased smart meter complexity or increased infrastructure complexity, which may render them unacceptable. The idea described in this paper aims to avoid those drawbacks by proposing a new architecture to implement privacy-aware metering. The proposed approach employs the concept of software-defined networking (SDN) to manage smart-meter data in a separate control layer inside the home network. Leveraging the concept of SDN puts the control back in the hands of the consumers to manage their own privacy via SDN applications, and relieves the supplier from enduring extra complexities.

The influence of differential privacy on short term electric load forecasting

There has been a large number of contributions on privacy-preserving smart metering with Differential Privacy, addressing questions from actual enforcement at the smart meter to billing at the energy provider. However, exploitation is mostly limited to application of cryptographic security means between smart meters and energy providers. We illustrate along the use case of privacy preserving load forecasting that Differential Privacy is indeed a valuable addition that unlocks novel information flows for optimization. We show that (i) there are large differences in utility along three selected forecasting methods, (ii) energy providers can enjoy good utility especially under the linear regression benchmark model, and (iii) households can participate in privacy preserving load forecasting with an individual membership inference risk <60%, only 10% over random guessing.

Differentially Private Smart Metering With Fault Tolerance and Range-Based Filtering

Smart grid enables two-way communications between operation centers and smart meters to collect power consumption and achieve demand response to improve flexibility, reliability, and efficiency of electricity system. However, power consumption data may contain users’ privacy, e.g., activities, references, and habits. Many smart metering schemes have been proposed utilizing homomorphic encryption for users’ privacy preservation. Unfortunately, some abnormality of smart meter reading, e.g., caused by electricity theft, cannot be discovered since data is encrypted. Meanwhile, operation centers could become curious in reality. To address the above issues, we propose a new privacy-preserving smart metering scheme for smart grid, which supports data aggregation, differential privacy, fault tolerance, and range-based filtering simultaneously. Specifically, we extend lifted ElGamal encryption to aggregate users’ consumption reports at the gateway to reduce communication overhead, while supporting fault tolerance of malfunctioning smart meters effectively. We also leverage zero-knowledge range proof to filter abnormal measurements caused by electricity theft or false data injection attacks without exposing individual measurements. In addition, our scheme can resist differential attacks, by which the curious operation center can violate users’ privacy through comparing two aggregations of the similar data set. Finally, we discuss the properties of the proposed scheme and evaluate its performance in terms of security and efficiency.

A New Framework for Privacy-Preserving Aggregation of Time-Series Data

Aggregator-oblivious encryption is a useful notion put forward by Shi et al. in 2011 that allows an untrusted aggregator to periodically compute an aggregate value over encrypted data contributed by a set of users. Such encryption schemes find numerous applications, particularly in the context of privacy-preserving smart metering. This article presents a general framework for constructing privacy-preserving aggregator-oblivious encryption schemes using a variant of Cramer-Shoup’s paradigm of smooth projective hashing. This abstraction leads to new schemes based on a variety of complexity assumptions. It also improves upon existing constructions, providing schemes with shorter ciphertexts and better encryption times.

Privacy-Preserving Smart Metering with Authentication in a Smart Grid

The traditional security objectives of smart grids have been availability, integrity, and confidentiality. However, as the grids incorporate smart metering and load management, user and corporate privacy is increasingly becoming an issue in smart grid networks. Although transmitting current power consumption levels to the supplier or utility from each smart meter at short intervals has an advantage for the electricity supplier’s planning and management purposes, it threatens user privacy by disclosing fine-grained consumption data and usage behavior to utility providers. In this study, we propose a distributed incremental data aggregation scheme where all smart meters on an aggregation path are involved in routing the data from the source meter to the collection unit. User privacy is preserved by symmetric homomorphic encryption, which allows smart meters to participate in the aggregation without seeing any intermediate or final result. Aggregated data is further integrated with an aggregate signature to achieve data integrity and smart meter authentication in such a way that dishonest or fake smart meters cannot falsify data en route. Only the collection unit can obtain the aggregated data and verify its integrity while the individual plain data are not exposed to the collection unit. Therefore, user privacy and security are improved for the smart metering in a smart grid network.

A Privacy-Preserving Smart Metering Scheme Using Linkable Anonymous Credential

Smart grid, as the next power grid, can efficiently monitor, predicate, and control energy generation/consumption by using the real-time users' electricity information. However, the fine-grained user energy consumption information may reveal the private information of the user. In this paper, we construct a linkable anonymous credential protocol based on Camenisch-Lysyanskaya (CL) signature. Then, we propose a privacy preserving smart metering scheme based on the new linkable anonymous credential. In addition to providing privacy protection for the user, our protocol also has the security properties of message authentication and traceability of fault smart metering. And there are some other useful features in our protocol, such as no need of trust-third party, dynamic users' enrollment and revocation, and complex statistical analysis of the energy use information. The computation cost and communication overhead of our scheme is O(1), which is independent of the user number. The simulation results show that our scheme is efficient.

Human-Factor-Aware Privacy-Preserving Aggregation in Smart Grid

Privacy-preserving metering aggregation is regarded as an important research topic in securing a smart grid. In this paper, we first identify and formalize a new attack, in which the attacker could exploit the information about the presence or absence of a specific person to infer his meter readings. This attack, coined as human-factor-aware differential aggregation (HDA) attack, cannot be addressed in existing privacy-preserving aggregation protocols proposed for smart grids. We give a formal definition on it and propose two novel protocols, including basic scheme and advanced scheme, to achieve privacy-preserving smart metering data aggregation and to resist the HDA attack. Our protocol ensures that smart meters periodically upload encrypted measurements to a (electricity) supplier/aggregator such that the aggregator is able to derive the aggregated statistics of all meter measurements but is unable to learn any information about the human activities. We present the formal security analysis for the proposed protocol to guarantee the strong privacy. Moreover, we evaluate the performance of our protocol in a Java-based implementation under different parameters. The performance and utility analysis shows that our protocol is simple, efficient, and practical.

Privacy-preserving smart metering with multiple data Consumers

The increasing diffusion of Automatic Meter Reading (AMR) and the possibility to open the system to third party services has raised many concerns about the protection of personal data related to energy, water or gas consumption, from which details about the habits of the users can be inferred.This paper proposes an infrastructure and a communication protocol for allowing utilities and third parties (data Consumers) to collect measurement data with different levels of spatial and temporal aggregation from smart meters without revealing the individual measurements to any single node of the architecture.The proposed infrastructure introduces a set of functional nodes in the smart grid, namely the Privacy Preserving Nodes (PPNs), which collect customer data encrypted by means of Shamir’s Secret Sharing Scheme, and are supposed to be controlled by independent parties. By exploiting the homomorphic properties of the sharing scheme, the measurements can be aggregated directly in the encrypted domain. Therefore, an honest-but-curious attacker can obtain neither disaggregated nor aggregated data. The PPNs perform different spatial and temporal aggregation for each Consumer according to its needs and access rights. The information Consumers recover the aggregated data by collecting multiple shares from the PPNs.The paper also discusses the problem of deploying the information flows from the customers to the PPNs and, then, to the information Consumers in a resource constrained environment. We prove that minimizing the number of PPNs is a NP-hard problem and propose a fast greedy algorithm. The scalability of the infrastructure is first analyzed under the assumption that the communication network is reliable and timely, then in presence of communication errors and node failures. The paper also evaluates the anonymity of external attackers.

A Randomized Response Model for Privacy Preserving Smart Metering

The adoption of smart meters may bring new privacy concerns to the general public. Given the fact that metering data of individual homes/factories is accumulated every 15 minutes, it is possible to infer the pattern of electricity consumption of individual users. In order to protect the privacy of users in a completely de-centralized setting (i.e., individuals do not communicate with one another), we propose a novel protocol, which allows individual meters to report the true electricity consumption reading with a pre-determinted probability. Load serving entities (LSE) can reconstruct the total electricity consumption of a region or a district through inference algorithm, but their ability of identifying individual users' energy consumption pattern is significantly reduced. Using simulated data, we verify the feasibility of the proposed method and demonstrate performance advantages over existing approaches.