Differentially private subspace clustering
Authors: Yining Wang, Yu-Xiang Wang, Aarti Singh
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate via both theory and experiments that one of the presented methods enjoys formal privacy and utility guarantees; the other one asymptotically preserves differential privacy while having good performance in practice. We provide numerical results of both the sample-aggregate and Gibbs sampling algorithms on synthetic and real-world datasets. |
| Researcher Affiliation | Academia | Yining Wang, Yu-Xiang Wang and Aarti Singh Machine Learning Department, Carnegie Mellon Universty, Pittsburgh, USA {yiningwa,yuxiangw,aarti}@cs.cmu.edu |
| Pseudocode | Yes | Algorithm 1 The sample-aggregate framework [22] Algorithm 2 Threshold-based subspace clustering (TSC), a simplified version |
| Open Source Code | No | The paper does not provide any explicit statements about the release of source code for the described methodology, nor does it include links to a code repository. |
| Open Datasets | Yes | We also experiment on real-world datasets. The right two plots in Figure 2 report utility on a subset of the extended Yale Face Dataset B [13] for face clustering. |
| Dataset Splits | No | The paper specifies dataset sizes (e.g., 'n = 5000' for synthetic, 'n = 320' for Yale Face Dataset B) but does not provide specific training, validation, or test split percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper states, 'All methods are implemented using Matlab,' but does not provide a specific version number for Matlab or any other software dependencies. |
| Experiment Setup | Yes | δ is set to 1/(n ln n) for (ε, δ)-privacy algorithms. s.a. stands for smooth sensitivity and exp. stands for exponential mechanism. Su LQ-10 and Su LQ-50 stand for the Su LQ framework performing 10 and 50 iterations. Gibbs sampling is run for 10000 iterations and the mean of the last 100 samples is reported. |