Differential Private Stream Processing of Energy Consumption
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A number of applications benefit from continuously releasing streams of personal data statistics. The process, however, poses significant privacy risks. Motivated by an application in energy systems, this paper presents OptStream, a novel algorithm for releasing differential private data streams. OptStream is a 4-step procedure consisting of sampling, perturbation, reconstruction, and post-processing modules. The sampling module selects a small set of points to access privately in each period of interest, the perturbation module adds noise to the sampled data points to guarantee privacy, the reconstruction module re-assembles the non-sampling data points from the perturbed sampled points, and the post-processing module uses convex optimization over the private output of the previous modules, as well as the private answers of additional queries on the data stream, to ensure consistency of the data's salient features. OptStream is used to release a real data stream from the largest transmission operator in Europe. Experimental results show that OptStream not only improves the accuracy of the state-of-the-art by at least one order of magnitude on this application domain, but it is also able to ensure accurate load forecasting based on the private data.
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