Counting Distinct Elements Under Person-Level Differential Privacy
Authors: Thomas Steinke, Alexander Knop
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In addition to proving the above theoretical guarantees, we perform an experimental evaluation of our algorithm. |
| Researcher Affiliation | Industry | Alexander Knop Google alexanderknop@google.com Thomas Steinke Google Deep Mind steinke@google.com |
| Pseudocode | Yes | Algorithm 1 Distinct Count Algorithm |
| Open Source Code | No | The paper does not provide any statement or link indicating that its source code is publicly available. |
| Open Datasets | Yes | We used four publicly available datasets to assess the accuracy of our algorithms compared to baselines. Two small datasets were used: Amazon Fashion 5-core [NLM19] (reviews of fashion products on Amazon) and Amazon Industrial and Scientific 5-core [NLM19] (reviews of industrial and scientific products on Amazon). Two large data sets were also used: Reddit [She20] (a data set of posts collected from r/Ask Reddit) and IMDb [N20; MDPHNP11] (a set of movie reviews scraped from IMDb). |
| Dataset Splits | No | The paper uses common datasets but does not provide specific details on how these datasets were split into training, validation, or testing sets for their experiments. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers used for implementation or experimentation. |
| Experiment Setup | Yes | Table 1: True and estimated (using DPDistinct Count with ε = 1, β = 0.05 and ℓmax = 100) counts per data set. |