Adversarially Robust Streaming Algorithms via Differential Privacy
Authors: Avinatan Hasidim, Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main conceptual contribution is to show that the notion of differential privacy can be used as a tool in order to construct new adversarially robust streaming algorithms. This connection allows us to design new adversarially robust streaming algorithms that outperform the current state-of-the-art constructions for many interesting regimes of parameters. |
| Researcher Affiliation | Collaboration | Avinatan Hassidim Bar-Ilan University and Google. Haim Kaplan Tel Aviv University and Google. Yishay Mansour Google. Yossi Matias Google. Uri Stemmer Ben-Gurion University and Google. |
| Pseudocode | Yes | Algorithm 1 Robust Sketch (Page 6) |
| Open Source Code | No | The paper does not provide any information about open-source code for the described methodology or links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on datasets, thus no information on training datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental setups involving data splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or system-level training settings. |