Adaptive Privacy Composition for Accuracy-first Mechanisms
Authors: Ryan M. Rogers, Gennady Samorodnitsk, Steven Z. Wu, Aaditya Ramdas
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our results to the task of releasing counts from a dataset subject to a relative error bound comparing the unified privacy filter and the baseline doubling approach, which uses the privacy filters from Whitehouse et al. [2023]. ... We perform experiments to return as many counts as possible subject to a relative error tolerance α and a privacy budget ε, δ > 0. We will generate synthetic data from a Zipf distribution ... We also evaluated our approach on real data from Reddit comments ... Our results are given in Figure 2 where we give the empirical average and standard deviation over 1000 trials for each sample size. |
| Researcher Affiliation | Collaboration | Ryan Rogers Linked In Gennady Samorodnitsky Cornell University Zhiwei Steven Wu Carnegie Mellon University Aaditya Ramdas Carnegie Mellon University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain an explicit statement about the release of source code for the methodology or a link to a code repository. |
| Open Datasets | Yes | We also evaluated our approach on real data from Reddit comments from https://github.com/heyyjudes/differentially-private-set-union/tree/ ea7b39285dace35cc9e9029692802759f3e1c8e8/data. |
| Dataset Splits | No | The paper mentions varying sample sizes for the data but does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., Python, PyTorch, scikit-learn versions) needed to replicate the experiment. |
| Experiment Setup | Yes | We will set a max value of the distribution to be 300 and a = 0.75. ... For the Exponential Mechanism, we select a fixed parameter εEM = 0.1. ... We also set an overall privacy budget of (ε, δ ) = (10, 10 6). ... We pick the smallest privacy parameter squared to be (ε(1) n )2 = 0.0001 for each n in both the noise reduction and the doubling method ... We then set 1000 equally spaced parameters in noise reduction ... We then vary the sample size of the data in {8000, 16000, 32000, 64000, 128000} ... using the Exponential Mechanism with εEM = 0.01, minimum privacy parameter ε(1) n = 0.0001, relative error α = 0.01, and overall (ε = 1, δ = 10 6)-DP guarantee. In 1000 trials... |