Differentially Private Equivalence Testing for Continuous Distributions and Applications
Authors: Or Sheffet, Daniel Omer
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present the first algorithm for testing equivalence between two continuous distributions using differential privacy (DP). Our algorithm is a private version of the algorithm of Diakonikolas et al [16]. |
| Researcher Affiliation | Academia | Daniel Omer Math. Dept. Bar-Ilan University omerdan@biu.ac.il Or Sheffet Faculty of Engineering Bar-Ilan University or.sheffet@biu.ac.il |
| Pseudocode | Yes | Algorithm 1 Private Equivalence Tester and Algorithm 2 Test Closeness-(N, m, X, Y , α, ϵ , δ , δ) |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and focuses on algorithm design and proofs, assuming oracle access to distributions for theoretical analysis rather than using specific publicly available datasets for training. |
| Dataset Splits | No | As the paper presents a theoretical framework without empirical studies, it does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | The paper focuses on theoretical contributions and does not describe any experimental setup or the specific hardware used. |
| Software Dependencies | No | The paper is theoretical and does not detail any software dependencies or their specific version numbers required for replication of experiments. |
| Experiment Setup | No | The paper describes a theoretical algorithm and its proofs without conducting empirical experiments; therefore, it does not include details on experimental setup or hyperparameters. |