Improved Bounds for Pure Private Agnostic Learning: Item-Level and User-Level Privacy
Authors: Bo Li, Wei Wang, Peng Ye
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we study pure private learning in the agnostic model a framework reflecting the learning process in practice. We examine the number of users required under item-level (where each user contributes one example) and user-level (where each user contributes multiple examples) privacy and derive several improved upper bounds. For item-level privacy, our algorithm achieves a near optimal bound for general concept classes. We extend this to the user-level setting, rendering a tighter upper bound than the one proved by Ghazi et al. (2023). Lastly, we consider the problem of learning thresholds under user-level privacy and present an algorithm with a nearly tight user complexity. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China. |
| Pseudocode | Yes | Algorithm 1 Private Threshold; Algorithm 2 Private Min Error |
| Open Source Code | No | The paper does not contain any statement about making source code publicly available or a link to a code repository for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on a specific dataset. It defines theoretical concepts of 'dataset' but does not refer to a concrete, publicly available dataset used for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental dataset splits for validation, training, or testing. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters, training configurations, or model initialization. |