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 [1].
Regression Model Fitting under Differential Privacy and Model Inversion Attack
Authors: Yue Wang, Cheng Si, Xintao Wu
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Theoretical analysis and empirical evaluations demonstrate our approach can effectively prevent model inversion attacks and retain model utility. In our experiments, we mainly focus on the problem of releasing the logistic regression model under differential privacy against model inversion attacks. We use the Adult dataset [Lichman, 2013] to evaluate the performance of Algorithm 1 and apply five-fold cross validation for all the accuracy calculation. |
| Researcher Affiliation | Academia | Yue Wang University of North Carolina at Charlotte Charlotte, NC, USA EMAIL Cheng Si University of Arkansas Fayetteville, AR, USA EMAIL Xintao Wu University of Arkansas Fayetteville, AR, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 Functional Mechanism with Different Perturbation of Coefficients |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the described methodology. |
| Open Datasets | Yes | We use the Adult dataset [Lichman, 2013] to evaluate the performance of Algorithm 1 and apply five-fold cross validation for all the accuracy calculation. |
| Dataset Splits | Yes | We use the Adult dataset [Lichman, 2013] to evaluate the performance of Algorithm 1 and apply five-fold cross validation for all the accuracy calculation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions general software concepts like 'logistic regression' and 'Laplace noise' but does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | In the second experiment, we set the privacy threshold ϵ = 1 and change the privacy budget ratio γ from {1, 0.5, 0.25, 0.1, 0.05, 0.025, 0.01}. Table 2 shows the corresponding ϵs and ϵn values under each γ. |