Applied Differential Privacy in the Smart Grid

Abstract

Privacy is an important guarantee to give to users in order for them to agree to release their, possibly sensitive, data for scientific or commercial purposes. However, guaranteeing privacy is not a trivial task. Previously there have been several cases where released data was believed to have been anonymized, where it later proved not to be anonymous at all [28, 38]. One methodology to be able to release anonymized calculations is differential privacy, where controlled noise is added to the calculation before it is release. However, there exists a trade-off between the privacy and the accuracy of the results when differential privacy is used. Previous work has mostly focused on differential privacy in theory, but there also exists work that applies differential privacy to a use case [32]. However, the utility of the differentially private results have not previously been evaluated when using only counting queries. In this thesis differential privacy is applied to one use case found in the smart grid, an evolved version of the electricity grid, to show that differential privacy is applicable in practice and not only in theory. The particular use case in this thesis compares a differentially private sum to the true sum, to estimate the error introduced by applying differential privacy. The results demonstrate that differential privacy shows promise also for realistic usage, providing privacy while still producing accurate results compared to the true results without differential privacy applied. For a setup with 1,000 simulated households, the best results for the mean error is between 0.42% and 0.59%, and the spread of the error ranged from 0% to 2.07%. All of these results have a confidence interval of 95%.