Abstract
As data privacy issues grow, finding the best privacy preservation algorithm for each situation is increasingly essential. This research has focused on understanding the frequency oracles (FO) privacy preservation algorithms. FO conduct the frequency estimation of any value in the domain. The aim is to explore how each can be best used and recommend which one to use with which data type. We experimented with different data scenarios and federated learning settings. Results showed clear guidance on when to use a specific algorithm.
| Original language | English |
|---|---|
| Pages (from-to) | 23642-23643 |
| Number of pages | 2 |
| Journal | Proceedings of the AAAI Conference on Artificial Intelligence |
| Volume | 38 |
| Issue number | 21 |
| DOIs | |
| State | Published - Mar 25 2024 |
| Event | 38th AAAI Conference on Artificial Intelligence, AAAI 2024 - Vancouver, Canada Duration: Feb 20 2024 → Feb 27 2024 |
Funding
This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725.