Comparing Population Means Under Local Differential Privacy: With Significance and Power
Authors: Bolin Ding, Harsha Nori, Paul Li, Joshua Allen
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report experimental results on real-world datasets to verify the effectiveness of our approaches. |
| Researcher Affiliation | Industry | Bolin Ding, Harsha Nori, Paul Li, Joshua Allen {bolind, hanori, paul.li, joshuaa}@microsoft.com Microsoft, One Microsoft Way, Redmond, WA 98052 |
| Pseudocode | Yes | Algorithm 1: Tbin ε : Transformation-based LDP Test; Algorithm 2: Tmix ε : For Hybrid Privacy Requirements |
| Open Source Code | No | The paper does not contain any statements about releasing open-source code or links to a code repository for the described methodology. |
| Open Datasets | No | There are 20 million users in this real-world dataset. Each user has a counter with the value in [0, 15000], i.e., m = 15000... We draw samples from control/treatment with equal sizes n A = n B. The paper mentions a 'real-world dataset' but does not provide a name, specific link, or citation for public access. |
| Dataset Splits | No | The paper discusses 'samples' and 'sample sizes' (n A, n B) but does not specify dataset splits for training, validation, or testing in the typical machine learning sense (e.g., percentages or counts for each split). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory, cloud instances) used to run the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | Parameters: privacy budget ε and significance level α... We vary the privacy parameter ε from 0.5 to 5. The pre-specified significance level α = 0.05, and the null hypothesis is H0 : μA μB = 0. |