Two Views of Constrained Differential Privacy: Belief Revision and Update
Authors: Likang Liu, Keke Sun, Chunlai Zhou, Yuan Feng
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In addition to the above analysis of privacy, we also perform analysis of utility and perform some experiments to show the differences between the two perspectives as belief revision and update. ...We performed some simulation experiments for larger n which showed similar results. We conjecture that this holds generally for any n and hence the variance of the marginal distribution by conditioning is smaller than that by imaging on this invariant for simple counting query. ...In this part, we experimentally compare accuracy between conditioning approach and imaging approach in processing data based on region hierarchy. In the experiment, we choose the improved MCMC method to obtain samples of the consistency constraint privacy mechanism, and compare it with the classic post-processing projection technique such as Top Down algorithm. We choose New York City Taxi Dataset for the experiment. ...Table 2. Accuracy Comparison of Algorithms Running on NY City Taxi Dataset at L1-distance |
| Researcher Affiliation | Academia | 1 School of Information, Renmin University of China, Beijing, China 2 Centre of Quantum Software and Information, University of Technology Sydney, Australia micahliu2012@gmail.com, skk2020@ruc.edu.cn, czhou@ruc.edu.cn, Yuan.feng@uts.edu.au |
| Pseudocode | No | The paper mentions and discusses algorithms like 'improved metropolis Hastings (MH) algorithm MMH' and 'Top Down algorithm', but it does not provide their pseudocode or algorithm blocks within the text. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | We choose New York City Taxi Dataset for the experiment. The specific selection is the yellow taxi trip dataset in February 2022. The relevant document is called yellow tripdata 2022-02.parquet while records all trip data of the iconic yellow taxi in New York City in February 2022. The dataset has 19 attribute columns, 2979431 record rows, where each row represents a taxi trip. |
| Dataset Splits | No | The paper describes the dataset used and its attributes but does not specify any training, validation, or test dataset splits (e.g., percentages, sample counts, or cross-validation setup) that would be needed for reproduction. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers, such as programming language versions, library versions, or solver versions. |
| Experiment Setup | Yes | Table 2 lists 'ϵ' values (1, 5, 10) under 'Accuracy Comparison of Algorithms Running on NY City Taxi Dataset at L1-distance'. These 'privacy budget' values are specific experimental parameters for differential privacy mechanisms. |