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 while learning only limited information about the activities of individual households. For example, a supplier cannot tell from a user's trace 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.
Several papers addressed the privacy problems of smart metering in the recent past @cite_9 @cite_8 @cite_20 @cite_19 @cite_11 @cite_1 @cite_14 @cite_0 . However, only a few of them have proposed technical solutions to protect users' privacy. In @cite_20 @cite_11 , the authors discuss the different security aspects of smart metering and the conflicting interests among stakeholders. The privacy of billing is considered in @cite_14 @cite_8 . These techniques uses zero-knowledge proofs to ensure that the fee calculated by the user is correct without disclosing any consumption data.
@cite_14 @cite_8 @cite_9 @cite_1 @cite_0 @cite_19 @cite_20 @cite_11
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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. The security and privacy of future smart grid and smart metering networks is important to their rollout and eventual acceptance by the public: research in this area is ongoing and smart meter users will need to be reassured that their data is secure. This paper describes a method for securely anonymizing frequent (for example, every few minutes) electrical metering data sent by a smart meter. Although such frequent metering data may be required by a utility or electrical energy distribution network for operational reasons, this data may not necessarily need to be attributable to a specific smart meter or consumer. It does, however, need to be securely attributable to a specific location (e.g. a group of houses or apartments) within the electricity distribution network. The method described in this paper provides a 3rd party escrow mechanism for authenticated anonymous meter readings which are difficult to associate with a particular smart meter or customer. This method does not preclude the provision of attributable metering data that is required for other purposes such as billing, account management or marketing research purposes. 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.