Private frequency estimation via projective geometry
Authors: Vitaly Feldman, Jelani Nelson, Huy Nguyen, Kunal Talwar
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical evaluation shows a speedup of over 50x over PI-RAPPOR while using approximately 75x less memory for practically relevant parameter settings. |
| Researcher Affiliation | Collaboration | 1Apple, Cupertino, CA, USA 2UC Berkeley, CA, USA 3Northeastern University, MA, USA. |
| Pseudocode | No | The paper describes algorithms mathematically and textually, particularly in Section 3 and 4, and mentions dynamic programming, but it does not include a dedicated 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | code and details on how to run the experiments used to generate data and plots are in our public repository at https://github.com/minilek/ private_frequency_oracles/. |
| Open Datasets | No | The paper mentions performing experiments on 'simple synthetic data' but does not provide any access information, generation method, or citation for a publicly available dataset. |
| Dataset Splits | No | The paper does not explicitly provide information about training, validation, or test dataset splits. It mentions using 'simple synthetic data' but no specific partitioning details. |
| Hardware Specification | Yes | All experiments were run on a Dell Precision T3600 with six Intel 3.2 GHz Xeon E5-1650 cores running Ubuntu 20.04 LTS |
| Software Dependencies | Yes | We implemented all algorithms and ran experiments in C++, using the GNU C++ compiler version 9.3.0 |
| Experiment Setup | Yes | We took ϵ = 5, a practically relevant setting, and n = 10,000, k = 3,307,948; this setting of n is smaller than one would see in practice, but the runtimes of the algorithms considered are all linear in n plus additional terms that depend on k, ϵ |