Efficient Error Prediction for Differentially Private Algorithms
Paper i proceeding, 2021
To fill the gap in the literature, we propose a novel application of factor experiments to create data aware error predictions. Basically, factor experiments provide a systematic approach to conducting empirical experiments. To demonstrate our methodology in action, we conduct a case study where error is dependent on arbitrarily complex tree structures. We first construct a tool to simulate poll data. Next, we use our simulated data to construct a least squares model to predict error. Last, we show how to validate the model. Consequently, our contribution is a method for constructing error prediction models that are data aware.
differential privacy
prediction model
data privacy
factor experiments
empirical evaluation
accuracy prediction
error prediction
Författare
Boel Nelson
Chalmers, Data- och informationsteknik, Informationssäkerhet
ACM International Conference Proceeding Series
3465746
9781450390514 (ISBN)
Vienna, Austria,
WebSec: Säkerhet i webb-drivna system
Stiftelsen för Strategisk forskning (SSF) (RIT17-0011), 2018-03-01 -- 2023-02-28.
Styrkeområden
Informations- och kommunikationsteknik
Ämneskategorier
Data- och informationsvetenskap
DOI
10.1145/3465481.3465746