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.
Furthermore, in @cite_0 a model for measuring privacy in Smart Metering is developed and subsequently two different solutions to privacy are presented: A Trusted Third Party-based approach, where individual consumption profiles are aggregated at the third party and only sums are communicated to the supplier. The other approach attempts to mask consumption profiles by adding randomness to the actual profile with an expectation of the random distribution of zero. In contrast to our solution, both of their approaches cannot handle billing of time-of-use tariffs but only provide either sums or not-accurate profiles. Furthermore, our approach does not require a trusted third party and provides exact results for every computation (as required by some legislations).
@cite_0
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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. The solutions offered to-date for end-user privacy in smart meter measurements, a well-known challenge in the smart grid, have been tied to specific technologies such as batteries or assumptions on data usage without quantifying the loss of benefit (utility) that results from any such approach. Using tools from information theory and a hidden Markov model for the measurements, a new framework is presented that abstracts both the privacy and the utility requirements of smart meter data. This leads to a novel privacy-utility tradeoff problem with minimal assumptions that is tractable. For a stationary Gaussian model of the electricity load, it is shown that for a desired mean-square distortion (utility) measure between the measured and revealed data, the optimal privacy-preserving solution: i) exploits the presence of high-power but less private appliance spectra as implicit distortion noise, and ii) filters out frequency components with lower power relative to a distortion threshold; this approach encompasses many previously proposed approaches to smart meter privacy. 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. 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.