Learning Model-Based Privacy Protection under Budget Constraints
Authors: Junyuan Hong, Haotao Wang, Zhangyang Wang, Jiayu Zhou7702-7710
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical studies on both synthetic and real datasets demonstrate that the proposed algorithm can achieve a superior utility with a given privacy constraint and generalize well to new private datasets distributed differently as compared to the hand-designed competitors. |
| Researcher Affiliation | Academia | 1 Department of Computer Science and Engineering Michigan State University, East Lansing, MI 48823, USA 2 Department of Electrical and Computer Engineering University of Texas at Austin, Austin TX 78712, USA |
| Pseudocode | Yes | Algorithm 1 Protected Stochastic Gradient Descent |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing the code for the work described in this paper, nor does it provide a direct link to a source-code repository for their methodology. |
| Open Datasets | Yes | IPUMS. IPMUS-International census database (Ruggles et al. 2018) and its processed version is from (Lee and Kifer 2018). ... MNIST35. The MNIST dataset (Lecun et al. 1998) includes 70, 000 gray-scale handwritten digits. |
| Dataset Splits | No | We randomly select 20% (80%) of the data for testing (training). ... We use the official training-testing splits for evaluating the learning algorithms". While training and testing splits are mentioned, specific details for a separate validation split (percentages, counts, or explicit methodology for how *their* data was partitioned for validation) are not provided. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | Unless otherwise specified, 20 units of hidden variables are used in each LSTM and ct CDP is the privacy measurement. ... The protector is trained by a total of 50 batches of 20 steps and 100 epochs (1 epoch includes 1 scheduler update and 5 projector updates). |