AAAI 2024technical0 citations
Frequency Oracle for Sensitive Data Monitoring (Student Abstract)
Richard Sances, Olivera Kotevska, Paul Laiu
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.
BibTeX
@article{Sances_Kotevska_Laiu_2024, title={Frequency Oracle for Sensitive Data Monitoring (Student Abstract)}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/30507}, DOI={10.1609/aaai.v38i21.30507}, abstractNote={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.}, number={21}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Sances, Richard and Kotevska, Olivera and Laiu, Paul}, year={2024}, month={Mar.}, pages={23642-23643} }