Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space
Authors: Adam Smith, Shuang Song, Abhradeep Guha Thakurta
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments: One important attribute of our algorithm is that the entire ο¬nal value of the sketch including all 1/Ξ³2 basic units but not the hash function descriptions is differentially private...In Section 3, we show that with Ξ΅ = 1.0, all three estimators reach nearly the same relative error as the non-private estimator, which is below 2% using 4096 hash functions. |
| Researcher Affiliation | Collaboration | Adam Smith Boston University EMAIL Shuang Song Google Research, Brain Team EMAIL Abhradeep Thakurta Google Research, Brain Team EMAIL |
| Pseudocode | Yes | Algorithm 1 AFM: Flajolet-Martin (FM) sketch for distinct elements |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for the described methodology or links to a code repository. |
| Open Datasets | No | The paper mentions 'We consider datasets with true cardinality F0(D) ranging approximately from 2^12 to 2^20 β 10^6.' but does not provide concrete access information, specific links, DOIs, repository names, or formal citations for any publicly available or open dataset. |
| Dataset Splits | No | The paper describes experiments and evaluation, but it does not specify any training, validation, or test dataset splits (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'For fair comparison, we implement all our algorithms in C++.' in Appendix E.1, but it does not provide specific ancillary software details such as library or solver names with version numbers. |
| Experiment Setup | Yes | We consider datasets with true cardinality F0(D) ranging approximately from 2^12 to 2^20 β 10^6. |