Local Private Hypothesis Testing: Chi-Square Tests

Authors: Marco Gaboardi, Ryan Rogers

ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental We then present experimental results showing the power of the different tests...Empirical Results. We then empirically compare the power between Local Noise GOF with Laplace noise in Algorithm 1, Local Gen RRGOF in Algorithm 3, and Local Bit Flip GOF in Algorithm 5.
Researcher Affiliation Academia 1 University at Buffalo, Buffalo, NY, USA 2University of Pennsylvania, Philadelphia, PA, USA.
Pseudocode Yes Algorithm 1 Locally Private GOF Test:Local Noise GOF
Open Source Code No The paper does not provide any concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper.
Open Datasets No The paper describes how data is simulated or generated based on statistical distributions ("sampled i.i.d. from Multinomial(1,p)") but does not provide concrete access information (link, DOI, repository, or formal citation with authors/year) for a publicly available or open dataset.
Dataset Splits No The paper describes experiments based on simulated data to evaluate statistical power and Type I error but does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, or testing.
Hardware Specification No The paper does not provide specific hardware details (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 like Python 3.8, CPLEX 12.4) needed to replicate the experiment.
Experiment Setup Yes In our experiments we fix α = 0.05 and ϵ {1, 2, 4}. ... For Local Noise GOF with Laplace noise, we will use m = 999 samples in our Monte Carlo simulations.