Breaking the Communication-Privacy-Accuracy Tradeoff with $f$-Differential Privacy
Authors: Richeng Jin, Zhonggen Su, caijun zhong, Zhaoyang Zhang, Tony Quek, Huaiyu Dai
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we examine the performance of the proposed ternary compressor in the case of distributed mean estimation. We follow the set-up of [9] and generate N = 1000 user vectors with dimension d = 250, i.e., x1, ..., x N R250. Each local vector has bounded l2 and l norms, i.e., ||xi||2 C = 1 and ||xi|| c = 1. Fig. 4 compares the proposed ternary stochastic compressor with SQKR and the Gaussian mechanism. |
| Researcher Affiliation | Academia | Richeng Jin1 Zhonggen Su1 Caijun Zhong1 Zhaoyang Zhang1 Tony Q.S. Quek2 Huaiyu Dai3 1Zhejiang University 2Singapore University of Technology and Design 3North Carolina State University |
| Pseudocode | Yes | Algorithm 1 Binomial Noise [6] ... Algorithm 2 Binomial Mechanism [9] ... Algorithm 3 Poisson Binomial Mechanism |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper states that data was generated for the experiments ('generate N = 1000 user vectors'), but does not provide access information (link, DOI, citation) for this generated dataset to make it publicly available or reproducible. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning into training, validation, or test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | In this section, we examine the performance of the proposed ternary compressor in the case of distributed mean estimation. We follow the set-up of [9] and generate N = 1000 user vectors with dimension d = 250, i.e., x1, ..., x N R250. Each local vector has bounded l2 and l norms, i.e., ||xi||2 C = 1 and ||xi|| c = 1. |