A Privacy-Preserving Noise Addition Data Aggregation Scheme for Smart Grid

Abstract

Smart meters are applied to the smart grid to report instant electricity consumption to servers periodically; these data enable a fine-grained energy supply. However, these regularly reported data may cause some privacy problems. For example, they can reveal whether the house owner is at home, if the television is working, etc. As privacy is becoming a big issue, people are reluctant to disclose this kind of personal information. In this study, we analyzed past studies and found that the traditional method suffers from a meter failure problem and a meter replacement problem, thus we propose a smart meter aggregation scheme based on a noise addition method and the homomorphic encryption algorithm, which can avoid the aforementioned problems. After simulation, the experimental results show that the computation cost on both the aggregator and smart meter side is reduced. A formal security analysis shows that the proposed scheme has semantic security.

Evaluation of Noise Distributions for Additive and Multiplicative Smart Meter Data Obfuscation

“…Obfuscation technique Smart grid Comments [16] Additive Yes Used Laplace distribution [17] Additive Yes Used Gaussian distribution [18] Additive Yes Used Gaussian distribution [23] Additive Yes Used Laplace distribution [24] Additive Yes Used uniform distribution [25] Miscellaneous Yes Used GMM [27] Additive Yes Used correlated Gaussian noise [28] Additive Yes Used Laplace distribution [29] Additive No Used controlled probabilistic noise [30] Additive No Studied noise addition [31] Multiplicative No Used truncated triangular distribution [32] Multiplicative No Used orthogonal matrices [33] Multiplicative No Showed that if original and perturbed data are highly correlated, a malicious entity can recover the original data [34] Multiplicative No Stated that multiplicative noise has the advantage of perturbation size directly proportional to the original data value [35] Multiplicative No Evaluated linear and non-linear schemes [36] Additive + Yes Showed that data hiding and additive noise could hinder consumer attributes idetification [37] Additive + Yes Used uniform noise followed by homomorphic encryption [38] Additive + No Either added random noise or augmented the data with fake noisy samples [39] Additive + No Studied obfuscation and anonymization, considered privacy but not utility [40] Miscellaneous Yes Used a sparse dictionary [41] Miscellaneous No Used GANs for privacy-reservation of mobile datasets [42] Miscellaneous Yes Studied linear and data shuffling masking methods that required knowledge of the whole data set [43] Miscellaneous Yes Replaced high-risk data with alternative data data by using linear regression for a large number of consumers.…”

“…The authors in [36] showed that by hiding some parts of the weekly consumption data of individual consumers, and adding artificial noise could decrease the accuracy of attribute identification, such as employment status, household family size, etc. In [37], the authors proposed a two-step scheme. First, an SM added uniform noise, followed by homomorphic encryption before sending the data to an aggregator.…”

Entropy-Based Anomaly Detection in Household Electricity Consumption

“…Analysis of the data can reveal sensitive information about the behavioral pattern of users, whether an appliance is on or not, whether the dwellers of the household are away, etc. [39]. Im et al [40] proposed a new method of electricity billing that preserves privacy while preserving data quality.…”

The Challenges of Privacy and Access Control as Key Perspectives for the Future Electric Smart Grid

“…Homomorphic encryption is another cryptographic technique widely used in data aggregation schemes in order to provide sufficient level of privacy to the smart meters. Based on this particular technique, the authors in [64] propose a privacy-preserving data aggregation scheme for the ESG, which enables smart meters to report their measurements periodically , while preventing private information from being leaked. Furthermore, homomorphic encryption is used in [65] as a part of a Privacy Preserving Fog-Enabled Aggregation (PPFA) scheme.…”