Numerical Composition of Differential Privacy
Authors: Sivakanth Gopi, Yin Tat Lee, Lukas Wutschitz
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the utility of our composition method by computing the privacy curves for the DP-SGD algorithm, which is one of the most important algorithms in differential privacy. Figure 1: Case study on DP-SGD. Sampling probability p = 10 3, noise scale σ = 0.8, δ = 10 7. |
| Researcher Affiliation | Collaboration | Sivakanth Gopi Microsoft Research sigopi@microsoft.com Yin Tat Lee University of Washington yintat@uw.edu Lukas Wutschitz Microsoft lukas.wutschitz@microsoft.com |
| Pseudocode | Yes | Algorithm 1: Compose PRV: Composing privacy curves using PRVs. Algorithm 2: Discretize PRV: Discretize and truncate a PRV |
| Open Source Code | Yes | Code is available at https://github.com/microsoft/prv_accountant. |
| Open Datasets | No | The paper discusses the DP-SGD algorithm and its parameters used in experiments but does not provide specific access information (link, DOI, repository, or formal citation) for any publicly available dataset used for their empirical evaluation. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | Yes | All experiments are performed on a Intel Xeon W-2155 CPU with 3.30GHz with 128GB of memory. |
| Software Dependencies | No | The paper mentions the use of Fast Fourier Transform (FFT) and references an implementation from prior work, but it does not provide specific software dependencies with version numbers (e.g., library names like PyTorch or NumPy along with their versions). |
| Experiment Setup | Yes | Sampling probability p = 10 3, noise scale σ = 0.8, δ = 10 7. |