Efficient Error Prediction for Differentially Private Algorithms
Paper in 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.
Chalmers, Computer Science and Engineering (Chalmers), Information Security
Proceedings of The 16th International Conference on Availability, Reliability and Security (ARES 2021)
WebSec: Securing Web-driven Systems
Swedish Foundation for Strategic Research (SSF) (RIT17-0011), 2018-03-01 -- 2023-02-28.
Areas of Advance
Information and Communication Technology
Computer and Information Science