# Estimating Smooth GLM in Non-interactive Local Differential Privacy Model with Public Unlabeled Data

arXiv: https://arxiv.org/abs/1910.00482 · HTML: https://mlsanity.com/paper/1910.00482/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2019.

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## Abstract (arXiv)

In this paper, we study the problem of estimating smooth Generalized Linear Models (GLMs) in the Non-interactive Local Differential Privacy (NLDP) model. Different from its classical setting, our model allows the server to access some additional public but unlabeled data. In the first part of the paper we focus on GLMs. Specifically, we first consider the case where each data record is i.i.d. sampled from a zero-mean multivariate Gaussian distribution. Motivated by the Stein's lemma, we present an (ε, δ)-NLDP algorithm for GLMs. Moreover, the sample complexity of public and private data for the algorithm to achieve an ℓ2-norm estimation error of α (with high probability) is O(p α-2) and tildeO(p3α-2ε-2) respectively, where p is the dimension of the feature vector. This is a significant improvement over the previously known exponential or quasi-polynomial in α-1, or exponential in p sample complexities of GLMs with no public data. Then we consider a more general setting where each data record is i.i.d. sampled from some sub-Gaussian distribution with bounded ℓ1-norm. Based on a variant of Stein's lemma, we propose an (ε, δ)-NLDP algorithm for GLMs whose sample complexity of public and private data to achieve an ℓ_∞-norm estimation error of α is O(p2α-2) and tildeO(p2α-2ε-2) respectively, under some mild assumptions and if α is not too small (em i.e., α≥ Omega(frac1√p)). In the second part of the paper, we extend our idea to the problem of estimating non-linear regressions and show similar results as in GLMs for both multivariate Gaussian and sub-Gaussian cases. Finally, we demonstrate the effectiveness of our algorithms through experiments on both synthetic and real-world datasets.