- By:
- Sances, Richard A; Kotevska, Olivera ; Laiu, Ming Tse P
- Page Number:
- 23642-23643
- Issue Number:
- 21
- Book Title:
- Proceedings of the AAAI Conference on Artificial Intelligence
- Publication Date:
- March 12, 2026
- Publisher Location:
- Association for the Advancement of Artificial Intelligence, United States of America
- Conference Name:
- Thirty-Eighth AAAI Conference on Artificial Intelligence
- Conference Location:
- Vancouver, Canada
- Conference Sponsor:
- Association for the Advancement of Artificial Intelligence
- View DOI Listing:
- https://doi.org/10.1609/aaai.v38i21.30507
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.
