Differentially Private Heatmaps
Authors: Badih Ghazi, Junfeng He, Kai Kohlhoff, Ravi Kumar, Pasin Manurangsi, Vidhya Navalpakkam, Nachiappan Valliappan
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our algorithm on both real-world location datasets and synthetic datasets. The results demonstrate its practicality even for moderate values of ε [0.5, 5] and a number of users equal to 200. Furthermore, we compare our algorithm with simple baselines; under popular metrics for heatmaps, our results demonstrate significant improvements on these regimes of parameters. |
| Researcher Affiliation | Industry | Google Research, Mountain View, CA {badihghazi, ravi.k53}@gmail.com {junfenghe,kohlhoff,pasin,vidhyan,nac}@google.com |
| Pseudocode | Yes | The paper includes 'Algorithm 1: DPSPARSEEMDAGG' and 'Algorithm 2: RECONSTRUCT' in pseudocode format. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We use two datasets available at snap.stanford.edu to generate the input distribution for users. The first dataset, called GOWALLA... Available at http://snap.stanford.edu/data/loc-Gowalla.html. The second dataset, called BRIGHTKITE... Available at http://snap.stanford.edu/data/loc-Brightkite.html. |
| Dataset Splits | No | The paper does not explicitly provide details about training, validation, and test dataset splits (e.g., percentages, sample counts, or citations to predefined splits) for the heatmaps. It describes sampling users for runs but not splitting the grid data into separate train/validation/test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU, or TPU models, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., 'Python 3.8, PyTorch 1.9') used for replicating the experiment. While it mentions 'TensorFlow Privacy' and 'PyTorch Differential Privacy' in related work, it does not state these were used for its own implementation with version numbers. |
| Experiment Setup | Yes | As for our parameters, we use the decay rate γ = 1/√2, which is obtained from minimizing the second error term in the proof of Theorem 3.1 as ℓ 5. We use w = 20 in our experiments, which turns out to work well already for datasets we consider. |