Jordan Awan
Pennsylvania State University
34 Papers
71 Citations
Jordan Awan is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Computer science & Differential privacy. The author has an hindex of 8, co-authored 25 publications. Previous affiliations of Jordan Awan include Purdue University & Clarion University of Pennsylvania.
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Papers
Structure and Sensitivity in Differential Privacy: Comparing K-Norm Mechanisms
TL;DR: This work addresses the problem of releasing a noisy real-valued statistic vector T, a function of sensitive data under DP, via the class of K-norm mechanisms via the goal of minimizing the noise added to achieve privacy.
47
•Proceedings Article
Differentially Private Uniformly Most Powerful Tests for Binomial Data
Jordan Awan,Aleksandra B. Slavkovic +1 more
- 23 May 2018
TL;DR: In this article, the authors derive uniformly most powerful (UMP) tests for simple and one-sided hypotheses for a population proportion within the framework of Differential Privacy (DP), optimizing finite sample performance.
Acoustic and Perceptual Classification of Within-sample Normal, Intermittently Dysphonic, and Consistently Dysphonic Voice Types.
TL;DR: Auditory-perceptual judgment of dysphonic segments and the typically robust acoustic measurement of mean CPP are both ineffective for classifying intermittently dysphonic voices, however, dysphonia duration may be effectively predicted via measures of the CPP distribution, and acoustic classification of dysphonics voice types via cepstral methods may be improved with an analysis of theCPP distribution across an utterance.
40
•Proceedings Article
Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA
Jordan Awan,Ana Kenney,Matthew Reimherr,Aleksandra B. Slavkovic +3 more
- 01 Jan 2019
TL;DR: A positive result is provided that establishes a Central Limit Theorem for the exponential mechanism quite broadly and the magnitude of the noise introduced for privacy is asymptotically non-negligible relative to the statistical estimation error.
The effect of gender on measures of electroglottographic contact quotient.
Shaheen N. Awan,Jordan Awan +1 more
TL;DR: The DEGG was the only method to show statistically significant differences in CQ useful in discriminating between "knee"-shaped opening phases observed in 76% of the men versus no "Knee" opening phases observation in 64% ofthe women.
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