Bridging Central and Local Differential Privacy in Data Acquisition Mechanisms
Authors: Alireza Fallah, Ali Makhdoumi, azarakhsh malekian, Asuman Ozdaglar
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We first establish minimax lower bounds for the estimation error, given a vector of privacy guarantees, and show that a linear estimator is (near) optimal. We then turn to our main goal: designing an optimal data acquisition mechanism. We establish that the design of such mechanisms in a Bayesian setting (where the platform knows the distribution of users sensitivities and not their realizations) can be cast as a nonconvex optimization problem. Additionally, for the class of linear estimators, we prove that finding the optimal mechanism admits a Polynomial Time Approximation Scheme. |
| Researcher Affiliation | Academia | Alireza Fallah EECS Department Massachusetts Institute of Technology afallah@mit.edu; Ali Makhdoumi Fuqua School of Business Duke University ali.makhdoumi@duke.edu; Azarakhsh Malekian Rotman School of Management University of Toronto azarakhsh.malekian@rotman.utoronto.ca; Asuman Ozdaglar EECS Department Massachusetts Institute of Technology asuman@mit.edu |
| Pseudocode | Yes | Algorithm 1: Computing the optimal two-part private data acquisition mechanism |
| Open Source Code | No | The paper explicitly states 'If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A]' and does not provide any links or statements about open-sourcing the code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical experiments. The 'If you ran experiments...' section states 'N/A' for questions related to code, data, and training details. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical experiments. The 'If you ran experiments...' section states 'N/A' for questions related to code, data, and training details. |
| Hardware Specification | No | The paper is theoretical and does not conduct empirical experiments. The 'If you ran experiments...' section states 'N/A' for questions related to hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not conduct empirical experiments. The 'If you ran experiments...' section states 'N/A' for questions related to training details and software dependencies. |
| Experiment Setup | No | The paper is theoretical and does not conduct empirical experiments. The 'If you ran experiments...' section states 'N/A' for questions related to training details and experiment setup. |