Model-Agnostic Private Learning
Authors: Raef Bassily, Om Thakkar, Abhradeep Guha Thakurta
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | 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... We show that our algorithm makes a conservative use of the privacy budget... 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. |
| Researcher Affiliation | Academia | Department of Computer Science & Engineering, The Ohio State University. bassily.1@osu.edu Department of Computer Science, Boston University. omthkkr@bu.edu Department of Computer Science, University of California Santa Cruz. aguhatha@ucsc.edu |
| Pseudocode | Yes | Algorithm 1 Astab [26]: Private release of a classification query via distance to instability; Algorithm 2 Abin Clas: Private Online Binary Classification via subsample and aggregate; and sparse vector; Algorithm 3 APriv: Private Learner |
| Open Source Code | No | The paper does not provide any links to open-source code or explicitly state that code for the described methodology is being released. |
| Open Datasets | No | The paper is theoretical and does not use specific, named, publicly available datasets. It refers to a 'private labeled dataset denoted by D' and 'unlabeled public data' but does not provide access information or citations for them. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments that would require specific training/validation/test dataset splits. It mentions 'standard validation techniques' in a remark, but not in the context of its own empirical setup. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or the hardware used to run experiments. |
| Software Dependencies | No | The paper is theoretical and does not describe any specific software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not describe specific experimental setup details, hyperparameters, or training configurations. |