We present the first differentially private framework for stochastic frontier analysis (SFA), addressing the challenge of non-convex objectives in privacy-preserving efficiency estimation. We construct a bounded parameter space to control gradient sensitivity and adapt the Frank–Wolfe algorithm with calibrated linear oracle noise to mitigate cumulative perturbation. Incorporating l1-regularization facilitates sparse and interpretable variable selection under strict (ϵ,δ)-differential privacy. Experiments demonstrate 15–35% MAE reduction under ϵ=0.1, along with strong scalability and estimation accuracy compared to prior DP methods for non-convex models.