A Knowledge Transfer Framework for Differentially Private Sparse Learning


  • Lingxiao Wang University of California, Los Angeles
  • Quanquan Gu University of California, Los Angeles




We study the problem of estimating high dimensional models with underlying sparse structures while preserving the privacy of each training example. We develop a differentially private high-dimensional sparse learning framework using the idea of knowledge transfer. More specifically, we propose to distill the knowledge from a “teacher” estimator trained on a private dataset, by creating a new dataset from auxiliary features, and then train a differentially private “student” estimator using this new dataset. In addition, we establish the linear convergence rate as well as the utility guarantee for our proposed method. For sparse linear regression and sparse logistic regression, our method achieves improved utility guarantees compared with the best known results (Kifer, Smith and Thakurta 2012; Wang and Gu 2019). We further demonstrate the superiority of our framework through both synthetic and real-world data experiments.




How to Cite

Wang, L., & Gu, Q. (2020). A Knowledge Transfer Framework for Differentially Private Sparse Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 6235-6242. https://doi.org/10.1609/aaai.v34i04.6090



AAAI Technical Track: Machine Learning