Frequency Oracle for Sensitive Data Monitoring (Student Abstract) Article Swipe

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Richard Sances , Olivera Kotevska , Paul Laiu · YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.1609/aaai.v38i21.30507

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.

Type

article

Language

en

Landing Page

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

PDF

https://ojs.aaai.org/index.php/AAAI/article/download/30507/32646

OA Status

diamond

References

3

Related Works

10

OpenAlex ID

https://openalex.org/W4393161283

Raw OpenAlex JSON