Continual Counting with Gradual Privacy Expiration
Authors: Joel Daniel Andersson, Monika Henzinger, Rasmus Pagh, Teresa Steiner, Jalaj Upadhyay
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical evaluation shows that we achieve a slowly growing privacy loss with significantly smaller empirical privacy loss for large values of d than a natural baseline algorithm. |
| Researcher Affiliation | Academia | Joel Daniel Andersson Basic Algorithms Research Copenhagen University of Copenhagen jda@di.ku.dk Monika Henzinger Institute of Science and Technology Austria Klosterneuburg, Austria monika.henzinger@ist.ac.at Rasmus Pagh Basic Algorithms Research Copenhagen University of Copenhagen pagh@di.ku.dk Teresa Anna Steiner University of Southern Denmark steiner@imada.sdu.dk Jalaj Upadhyay Rutgers University jalaj.upadhyay@rutgers.edu |
| Pseudocode | Yes | Algorithm 1 Asimple: Continual counting under linear gradual privacy expiration; Algorithm 2 Alog: Continual counting under logarithmic gradual privacy expiration; Algorithm 3 Continual counting, gradual privacy expiration |
| Open Source Code | Yes | The code for reproducing the result is provided as a zip file containing documentation and Python code. In particular, executing the included Jupyter Notebook will reproduce our figures. |
| Open Datasets | No | As for both algorithms, the privacy loss does not depend on the input data; we used an all-zero input stream, a standard approach in the industry (see, for example, Thakurta s [39] plenary talk at USENIX, 2017). |
| Dataset Splits | No | The paper uses an 'all-zero input stream' for empirical evaluation of privacy loss, which does not involve traditional training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, or cloud resources) used for conducting the experiments. |
| Software Dependencies | No | The NeurIPS Paper Checklist states that 'The code for reproducing the result is provided as a zip file containing documentation and Python code. In particular, executing the included Jupyter Notebook will reproduce our figures.' However, no specific version numbers for Python or any libraries are mentioned. |
| Experiment Setup | Yes | For all runs of Algorithm 3 we used B = 0, as for larger values of B, the primary effect would be to shift the privacy loss curve to the right. We picked the MSE to be 1000 for all plots as it leads to small values of the empirical privacy loss. As for both algorithms, the privacy loss does not depend on the input data; we used an all-zero input stream... In all the plots, we choose the privacy parameter(s) to achieve a mean-squared error of 1000 over the stream length T in each figure shown, where T = 103 for Figure 3(a), 3(b) and 3(c), and T = 106 in Figure 4. The corresponding privacy parameters are listed in Table 1, ε refers to the privacy parameter used by Algorithm 3 and εcur, εpast are the ones used by the baseline. |