Differentially Private Logistic Regression and its characterization for big data privacySpeaker: Shan Suthaharan – Greensboro, NC, United States
Topic(s): Security and Privacy
AbstractThis talk will present the topics that enlighten data owners on the selection processes of privacy parameters for differentially private logistic regression (DPLR) techniques. This knowledge will help them achieve a balance between privacy strength and classification accuracy. The talk will introduce a newly proposed approach that implements a supervised learning technique and a feature extraction technique to address this challenging problem. The discussion will include how the supervised learning technique selects subspaces from a training data set and generates DPLR classifiers for a range of values of the privacy parameter. It will also elaborate the feature extraction technique that transforms an original subspace to a differentially private subspace by querying the original subspace multiple times using the DPLR model and the privacy parameter values that were selected by the supervised learning module. The signal-interference-ratio, a popular signal processing technique - is a major player for quantifying the privacy level of the differentially private subspaces; hence, it will be discussed in detail in the lecture. It is especially used as a measure to allow data owners learn the privacy level that the DPLR models can provide for a given subspace and a given classification accuracy.
About this LectureNumber of Slides: 30
Duration: 60 minutes
Languages Available: English
Request this Lecture
To request this particular lecture, please complete this online form.
Request a Tour
To request a tour with this speaker, please complete this online form.
All requests will be sent to ACM headquarters for review.