Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Local Differential Privacy for Belief Functions
Authors: Qiyu Li, Chunlai Zhou, Biao Qin, Zhiqiang Xu10025-10033
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulation experiments are carried out to verify the trade-off in the privacy mechanism. |
| Researcher Affiliation | Academia | Qiyu Li1, Chunlai Zhou1*, Biao Qin1, Zhiqiang Xu2 1 Computer Science Dept., Renmin University of China, Beijing, CHINA 2 Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about making its source code available or provide a link to a code repository. |
| Open Datasets | No | A simple random sample of n people is drawn with replacement from the population. Let Zi denote the i-th sample element. |
| Dataset Splits | No | We set the sample size to be 10, 100, 500, 1000 and fix q3 = 0.1. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processors, or memory used for running experiments. |
| Software Dependencies | No | The paper does not provide any specific software details, such as library names or solver versions. |
| Experiment Setup | Yes | Simulation experiments are carried out to verify the trade-off in the privacy mechanism. In order to reduce the sampling error on the experimental results, the following results are the average of 1000 experimental outcomes. We set the sample size to be 10, 100, 500, 1000 and fix q3 = 0.1. Let π be the true proportion of the people having property P. A sample of Y1, , Yn of respondents are drawn with replacement from the population and their responses are distributed i.i.d. according to (q1, q2) = (π, 1 π)QW. Q2 3 = p q 1 p q q p 1 p q where p, q [0, 1]. |