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 | Conference PDF | Archive PDF | Plain Text | 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 fuheng_zhao@ucsb.edu Dan Qiao danqiao@ucsb.edu Rachel Redberg rredberg@ucsb.edu Divyakant Agrawal agrawal@cs.ucsb.edu Amr El Abbadi amr@cs.ucsb.edu Yu-Xiang Wang yuxiangw@ucsb.edu 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%. |