User-Level Differential Privacy With Few Examples Per User
Authors: Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work we consider the example-scarce regime, where each user has only a few examples, and obtain the following results: For approximate-DP, we give a generic transformation of any item-level DP algorithm to a user-level DP algorithm. ...For pure-DP, we present a simple technique for adapting the exponential mechanism [MT07] to the user-level setting. ... We will be intentionally vague; all definitions and results will be formalized later in the paper. At a high-level, our proof proceeds roughly as follows. First, we show that any (ε, δ)-item-level DP A with high probability satisfies a local version of user-level DP... The remainder of this section is devoted to the proof of Theorem 10. |
| Researcher Affiliation | Collaboration | Badih Ghazi Google Research Mountain View, CA, US badihghazi@gmail.com Pritish Kamath Google Research Mountain View, CA, US pritish@alum.mit.edu Ravi Kumar Google Research Mountain View, CA, US ravi.k53@gmail.com Pasin Manurangsi Google Research Bangkok, Thailand pasin@google.com Raghu Meka UCLA Los Angeles, CA, US raghum@cs.ucla.edu Chiyuan Zhang Google Research Mountain View, CA, US chiyuan@google.com |
| Pseudocode | Yes | Algorithm 1 Del Stabε,δ,A(x) |
| Open Source Code | No | The paper does not provide any specific links to source code or explicitly state that the code is publicly available. |
| Open Datasets | No | The paper is theoretical, defining input 'x Dnm' as 'nm i.i.d. samples drawn from D'. It does not mention any specific, named public datasets, nor provide links or citations for accessing data for experimental purposes. |
| Dataset Splits | No | The paper is theoretical and focuses on mathematical derivations and bounds. It does not describe any experimental setup involving dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup, therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any experimental setup that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and theoretical bounds. It describes mathematical parameters for algorithms (e.g., ε, δ, m) but does not provide practical experimental setup details like hyperparameter values, training schedules, or specific model architectures. |