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..
Differentially Private Linear Sketches: Efficient Implementations and Applications
Authors: Fuheng Zhao, Dan Qiao, Rachel Redberg, Divyakant Agrawal, Amr El Abbadi, Yu-Xiang Wang
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have implemented DP linear sketches and DP DCS, and conducted extensive experiments to evaluated the privacy-utility trade-off of our proposed private sketches. |
| Researcher Affiliation | Academia | Fuheng Zhao EMAIL Dan Qiao EMAIL Rachel Redberg EMAIL Divyakant Agrawal EMAIL Amr El Abbadi EMAIL Yu-Xiang Wang EMAIL Department of Computer Science, UC Santa Barbara. |
| Pseudocode | Yes | Algorithm 1 Linear Sketch Update(x, v), Algorithm 2 Linear Sketch Query(x), Algorithm 3 DP Linear Sketch Initialization with Gaussian Noise |
| Open Source Code | Yes | The code for the following experiments can be found on Github 3. (Footnote 3: https://github.com/ZhaoFuheng/Differentially-Private-Linear-Sketches) |
| Open Datasets | Yes | We consider the synthetic Zipf dataset Zipf [2016] with universe size of 2^16 and the source IP addresses from CAIDA Anonymized Internet Trace 2015 dataset pas with universe size of 2^32. (Bibliography entry: Anonymized internet traces 2015. https://catalog.caida.org/details/dataset/passive_ 2015_pcap. Accessed: 2022-5-10.) |
| Dataset Splits | No | The paper mentions an input database size N = 10^5, but does not provide explicit training, validation, and test dataset splits, percentages, or methodology for partitioning data. |
| Hardware Specification | Yes | we didn t use any external resources beside a macbook pro. |
| Software Dependencies | No | The paper states 'The implementations are written in Python' but does not specify the Python version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | The experiments assume β = 1% and N = 10^5. The DP DCS use privacy budget ε ∈ {0.1, 1, 10} and all sketches assume γ = 1%. |