Traditional electricity meters are replaced by Smart Meters in customers' households. Smart Meters collects fine-grained utility consumption profiles from customers, which in turn enables the introduction of dynamic, time-of-use tariffs. However, the fine-grained usage data that is compiled in this process also allows to infer the inhabitant's personal schedules and habits. We propose a privacy-preserving protocol that enables billing with time-of-use tariffs without disclosing the actual consumption profile to the supplier. Our approach relies on a zero-knowledge proof based on Pedersen Commitments performed by a plug-in privacy component that is put into the communication link between Smart Meter and supplier's back-end system. We require no changes to the Smart Meter hardware and only small changes to the software of Smart Meter and back-end system. In this paper we describe the functional and privacy requirements, the specification and security proof of our solution and give a performance evaluation of a prototypical implementation.
Finally, in @cite_19 also a twofold approach is presented: The first solution employs a sophisticated Trusted Platform Module (TPM) in the Smart Meter to obtain signed tariff data from the supplier and calculate a trustworthy bill. The second solution makes use of the electrical grid infrastructure as a third party to anonymize up-to-date consumption values sent out constantly by Smart Meters. Our approach can be distinguished as it only addresses billing but only requires a very simple TPM that creates commitments.
@cite_19
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Privacy-preserving billing protocols are useful in settings where a meter measures user consumption of some service, such as smart metering of utility consumption, pay-as-you-drive insurance and electronic toll collection. In such settings, service providers apply fine-grained tariff policies that require meters to provide a detailed account of user consumption. The protocols allow the user to pay to the service provider without revealing the user’s consumption measurements. Our contribution is twofold. First, we propose a general model where a meter can output meter readings to multiple users, and where a user receives meter readings from multiple meters. Unlike previous schemes, our model accommodates a wider variety of smart metering applications. Second, we describe a protocol based on polynomial commitments that improves the efficiency of previous protocols for tariff policies that employ splines to compute the price due. Smart grid proposals threaten user privacy by potentially disclosing fine-grained consumption data to utility providers, primarily for time-of-use billing, but also for profiling, settlement, forecasting, tariff and energy efficiency advice. We propose a privacy-preserving protocol for general calculations on fine-grained meter readings, while keeping the use of tamper evident meters to a strict minimum. We allow users to perform and prove the correctness of computations based on readings on their own devices, without disclosing any fine grained consumption. Applying the protocols to time-of-use billing is particularly simple and efficient, but we also support a wider variety of tariff policies. Cryptographic proofs and multiple implementations are used to show the proposed protocols are secure and efficient. In this paper, we consider a smart power infrastructure, where several subscribers share a common energy source. Each subscriber is equipped with an energy consumption controller (ECC) unit as part of its smart meter. Each smart meter is connected to not only the power grid but also a communication infrastructure such as a local area network. This allows two-way communication among smart meters. Considering the importance of energy pricing as an essential tool to develop efficient demand side management strategies, we propose a novel real-time pricing algorithm for the future smart grid. We focus on the interactions between the smart meters and the energy provider through the exchange of control messages which contain subscribers' energy consumption and the real-time price information. First, we analytically model the subscribers' preferences and their energy consumption patterns in form of carefully selected utility functions based on concepts from microeconomics. Second, we propose a distributed algorithm which automatically manages the interactions among the ECC units at the smart meters and the energy provider. The algorithm finds the optimal energy consumption levels for each subscriber to maximize the aggregate utility of all subscribers in the system in a fair and efficient fashion. Finally, we show that the energy provider can encourage some desirable consumption patterns among the subscribers by means of the proposed real-time pricing interactions. Simulation results confirm that the proposed distributed algorithm can potentially benefit both subscribers and the energy provider. This paper presents a new privacy-preserving smart metering system. Our scheme is private under the differential privacy model and therefore provides strong and provable guarantees.With our scheme, an (electricity) supplier can periodically collect data from smart meters and derive aggregated statistics without learning anything about the activities of individual households. For example, a supplier cannot tell from a user's trace whether or when he watched TV or turned on heating. Our scheme is simple, efficient and practical. Processing cost is very limited: smart meters only have to add noise to their data and encrypt the results with an efficient stream cipher.