The Composition Theorem for Differential Privacy
Authors: Peter Kairouz, Sewoong Oh, Pramod Viswanath
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our solution is complete: we prove an upper bound on the overall privacy level and construct a sequence of privatization mechanisms that achieves this bound. The key innovation is the introduction of an operational interpretation of differential privacy (involving hypothesis testing) and the use of new data processing inequalities. |
| Researcher Affiliation | Academia | Peter Kairouz KAIROUZ2@ILLINOIS.EDU ECE Department, University of Illinois at Urbana-Champaign, Urbana, IL 61801 USA Sewoong Oh SWOH@ILLINOIS.EDU IESE Department, University of Illinois at Urbana-Champaign, Urbana, IL 61801 USA Pramod Viswanath PRAMODV@ILLINOIS.EDU ECE Department, University of Illinois at Urbana-Champaign, Urbana, IL 61801 USA |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link for the open-sourcing of the code for the described methodology. |
| Open Datasets | No | The paper is theoretical in nature, focusing on proofs and mathematical characterizations of differential privacy composition. It does not mention or use specific datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental validation on data. Therefore, it does not specify any dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments requiring specific hardware. No hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical proofs and characterizations. It does not describe any experimental setup details such as hyperparameters or training configurations. |