The Target-Charging Technique for Privacy Analysis across Interactive Computations
Authors: Edith Cohen, Xin Lyu
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate numerically the benefits of our TCT-based Between Thresholds (Section 2.6) compared with the SVT-based prior work of [3]. Figure 1 shows the dependence of the number of between responses (y axis) on the number of data points n (x axis) for TCT and for the provided and optimistic bounds for [3]. |
| Researcher Affiliation | Collaboration | Edith Cohen Google Research and Tel Aviv University edith@cohenwang.com Xin Lyu UC Berkeley lyuxin1999@gmail.com |
| Pseudocode | Yes | Algorithm 1: Target Charging ... Algorithm 2: Conditional Release and Revise Calls ... Algorithm 3: One-Shot Top-k Selection ... Algorithm 4: Boundary Wrapper ... Algorithm 5: SVT with Individual Privacy Charging ... Algorithm 6: Target Charging with Approximate DP ... Algorithm 7: Simulation of Target Charging ... Algorithm 8: Multi-Target Charging ... Algorithm 9: Private Selection: A Simulation |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that open-source code for the described methodology is provided. |
| Open Datasets | No | The paper mentions a generic 'sensitive dataset D = {x1, . . . , xn}' in Section H for numerical comparison. It does not refer to a specific, publicly available dataset with concrete access information (e.g., URL, DOI, or formal citation). |
| Dataset Splits | No | The paper does not provide specific details about training, validation, or test dataset splits. The numerical comparison in Section H uses a generic 'dataset D = {x1, . . . , xn}' without specifying any splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments, including the numerical comparison in Section H. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate any numerical results or theoretical derivations. |
| Experiment Setup | No | The paper does not contain specific experimental setup details, such as concrete hyperparameter values or training configurations, as its primary focus is on theoretical privacy analysis. The parameters mentioned in Section H (ε, δ, α) relate to the theoretical bounds rather than an empirical training setup. |