A Joint Exponential Mechanism For Differentially Private Top-$k$
Authors: Jennifer Gillenwater, Matthew Joseph, Andres Munoz, Monica Ribero Diaz
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments compare the peeling and joint mechanisms across several real-world datasets using the error metrics from Definition 2.2. |
| Researcher Affiliation | Collaboration | 1Google Research NYC 2UT Austin. |
| Pseudocode | Yes | Algorithm 1 Efficiently sampling JOINT |
| Open Source Code | Yes | All datasets and experiment code are public (Google, 2022). |
| Open Datasets | Yes | We use six datasets: Books (Soumik, 2019) (11,000+ Goodreads books with review counts), Foods (Mc Auley, 2014) (166,000+ Amazon foods with review counts), Games (Tamber, 2016) (5,000+ Steam games with purchase counts), Movies (Harper & Konstan, 2015) (62,000+ Movies with rating counts), News (Fernandes et al., 2015) (40,000+ Mashable articles with share counts), and Tweets (Bin Tareaf, 2017) (52,000+ Tweets with like counts). |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We use k = 5, 15, . . . , 195 with 1-DP instances of JOINT and PNF-PEEL and (1, 10 6)-DP instances of CDP-PEEL. |