Computation of privacy-preserving prices in smart grids

Demand management through pricing is a modern approach that can improve the efficiency of modern power networks. However, computing optimal prices requires access to data that individuals consider private. We present a novel approach for computing prices while providing privacy guarantees under the differential privacy framework. Differentially private prices are computed through a distributed utility maximization problem with each individual perturbing their own utility function. Privacy concerning temporal localization and monitoring of an individual´s activity is enforced in the process. The proposed scheme provides formal privacy guarantees and its performance-privacy trade-off is evaluated quantitatively.