Mean Estimation in the Add-Remove Model of Differential Privacy
Authors: Alex Kulesza, Ananda Theertha Suresh, Yuyan Wang
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Figure 3 we plot the empirical performance of the algorithms discussed in Sections 3 and 4.1 on synthetic datasets and explore how the performance changes with parameters such as the privacy budget ε and the true mean µ. The underlying datasets are generated i.i.d. with varying µ in the range [ℓ= 0, u = 1]. All datasets have 10, 000 points, and mean squared error is computed over 100, 000 runs of each algorithm. |
| Researcher Affiliation | Industry | 1Google Research, NYC. |
| Pseudocode | Yes | Algorithm 1 Independent noise addition. Input: Multiset D [l, u], ε > 0. ... Algorithm 2 Shifted noise addition. Input: Multiset D [l, u], ε > 0. ... Algorithm 3 Transformed noise addition. Input: Multiset D [l, u], ε > 0. |
| Open Source Code | No | No explicit statement or link indicating the provision of open-source code for the methodology described in this paper. |
| Open Datasets | No | The underlying datasets are generated i.i.d. with varying µ in the range [ℓ= 0, u = 1]. |
| Dataset Splits | No | No specific dataset split information (percentages, sample counts, or citations to predefined splits) for training, validation, or testing is provided. |
| Hardware Specification | No | No specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running experiments are provided. |
| Software Dependencies | No | No specific ancillary software details (e.g., library or solver names with version numbers) are provided. |
| Experiment Setup | Yes | The underlying datasets are generated i.i.d. with varying µ in the range [ℓ= 0, u = 1]. All datasets have 10, 000 points, and mean squared error is computed over 100, 000 runs of each algorithm. |