Paper:
Model-Agnostic Private Learning
We design differentially private learning algorithms that are agnostic to the learning model assuming access to a limited amount of unlabeled public data. First, we provide a new differentially private algorithm for answering a sequence of m online classification queries (given by a sequence of m unlabeled public feature vectors) based on a private training set. Our algorithm follows the paradigm of subsample-and-aggregate, in which any generic non-private learner is trained on disjoint subsets of the private training set, and then for each classification query, the votes of the resulting classifiers ensemble are aggregated in a differentially private fashion. Our private aggregation is based on a novel combination of the distance-to-instability framework [26], and the sparse-vector technique [15,18]. We show that our algorithm makes a conservative use of the privacy budget. In particular, if the underlying non-private learner yields a classification error of at most α ∈ (0, 1), then our construction answers more queries, by at least a factor of 1/α in some cases, than what is implied by a straightforward application of the advanced composition theorem for differential privacy. Next, we apply the knowledge transfer technique to construct a private learner that outputs a classifier, which can be used to answer an unlimited number of queries. In the PAC model, we analyze our construction and prove upper bounds on the sample complexity for both the realizable and the non-realizable cases. Similar to non-private sample complexity, our bounds are completely characterized by the VC dimension of the concept class.

Review:
The paper considers a new differentially private learning setting, that receives a collection of unlabeled public data, on top of the labelled private data of interests and assumes that the two data sets are drawn from the same distribution. The proposed technique allows the use of (non-private) agnostic PAC learners as black boxes oracles, which, when combining with  and adapts to the structure of the data sets.  The idea is summarized below: 1. Do differentially private model-serving in a data-adaptive fashion, through ``sparse vector’’ technique and ``subsample-and-aggregate’’.  This only handles a finite number of classification queries. 2. Reduction: Use the labeled private data and non-private blackbox learner to differentially privately release labels, so the released model is DP w.r.t. the private data since it is just post-processing. It behaves similarly to 1 using the properties of an agnostic PAC learner, but can now handle an unbounded number of classification queries.  The idea is not entirely new but the particular approach with Algorithm 1 and formal theoretical analysis is (See Point 1 in detailed comments).   My only concern is that it is hard to compare the results to the standard non-adaptive DP-learning setting without unlabeled data and their respective upper/lower bounds.  (See Point 2 in my detailed comments)  Other than that, I like the work and I recommend accepting it for NIPS.    Detailed comments:   1. The PATE algorithm used in  - Papernot et al. in "Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data" (ICLR 2017, https://arxiv.org/abs/1610.05755).  is about the same algorithmically, although Papernot et. al. focuses on empirical evaluation.   The differentially private model-serving part is closely related to   - Dwork, C., & Feldman, V. (2018). Privacy-preserving Prediction. arXiv preprint arXiv:1803.10266.  That said, the authors did acknowledge the above works and claim that they are independent.   2. One of the main motivation of the work, according to Line 35-37 is to “characterize … the improvements one can achieve for private learning … ” (with the additional unlabeled data) “compared to the aforementioned setting” (the setting without such data).  I could not find a theorem in the paper that separates the two settings in terms of their sample-complexity. Maybe I missed it somewhere?   Intuitively, there shouldn’t be much difference, especially in the setting when m is about the same as n. Because one can throw away half of the labels in the standard setting so it becomes the setting here with half the data. The only concerning matter is that it is not clear whether the proposed algorithm actually attains the lower bound in the standard setting because the nature of the guarantees are very different, and the current results need to work for all (agnostic) PAC learners.  Is logistic regression is an agnostic PAC learner from the surrogate loss point of view?   Post rebuttal comments:  - I admit having written something misleading in the review and I apologize. Indeed the setting is easier than the standard private learning, where both features and labels need to be protected.   - My comment that the bounds are not compatible still hold, because this paper works with universal learner oracles, while in standard DP learning, the metric of interest is about empirical excess risk (defined by any convex loss, rather than the 0-1 loss here).  - The point about "throwing away a random half of the labels" is in fact about the label-private learning setting. What I would like the authors to compare is the proposed approach and the alternative approach, e.g., that by Chaudhuri and Hsu, which directly analyze the label-privacy for either output perturbation or exponential mechanism.  The advantage of the "model agnostic" approach is not clear to me.  Regardless, this is a much better paper relative to other papers in my stack.  I trust that the authors will do a good revision and clarify these matters in the camera-ready version.