ML Anthology

Frequency Oracle for Sensitive Data Monitoring (Student Abstract)

Richard Sances, Olivera Kotevska, Paul Laiu
AAAI 2024 pp. 23642-23643
doi:10.1609/AAAI.V38I21.30507 /aaai/2024/sances2024aaai-frequency/

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.

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Cite

Text

Sances et al. "Frequency Oracle for Sensitive Data Monitoring (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I21.30507

Markdown

[Sances et al. "Frequency Oracle for Sensitive Data Monitoring (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/sances2024aaai-frequency/) doi:10.1609/AAAI.V38I21.30507

BibTeX

@inproceedings{sances2024aaai-frequency,
  title     = {{Frequency Oracle for Sensitive Data Monitoring (Student Abstract)}},
  author    = {Sances, Richard and Kotevska, Olivera and Laiu, Paul},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2024},
  pages     = {23642-23643},
  doi       = {10.1609/AAAI.V38I21.30507},
  url       = {https://mlanthology.org/aaai/2024/sances2024aaai-frequency/}
}