Frequency Oracle for Sensitive Data Monitoring (Student Abstract)

Authors

  • Richard Sances Virginia Polytechnic Institute and State University
  • Olivera Kotevska Oak Ridge National Laboratory
  • Paul Laiu Oak Ridge National Laboratory

DOI:

https://doi.org/10.1609/aaai.v38i21.30507

Keywords:

Federated Learning, Local Differential Privacy, Frequency Oracles

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.
AAAI-24 / IAAI-24 / EAAI-24 Proceedings Cover

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Published

2024-03-24

How to Cite

Sances, R., Kotevska, O., & Laiu, P. (2024). Frequency Oracle for Sensitive Data Monitoring (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23642–23643. https://doi.org/10.1609/aaai.v38i21.30507

Issue

Vol. 38 No. 21: IAAI-24, EAAI-24, AAAI-24 Student Abstracts, Undergraduate Consortium and Demonstrations

Section

AAAI Student Abstract and Poster Program