  This paper considers the problem of releasing privacy-preserving load data of
a decentralized operated power system. The paper focuses on data used to solve
Optimal Power Flow (OPF) problems and proposes a distributed algorithm that
complies with the notion of Differential Privacy, a strong privacy framework
used to bound the risk of re-identification. The problem is challenging since
the application of traditional differential privacy mechanisms to the load data
fundamentally changes the nature of the underlying optimization problem and
often leads to severe feasibility issues. The proposed differentially private
distributed algorithm is based on the Alternating Direction Method of
Multipliers (ADMM) and guarantees that the released privacy-preserving data
retains high fidelity and satisfies the AC power flow constraints. Experimental
results on a variety of OPF benchmarks demonstrate the effectiveness of the
approach.
