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Friday, October 4
Fri, Oct 4, 11:45 AM - 1:15 PM
Evergreen I
Speed Session 3

Detecting Personally Identifiable Information Attempt Violations in an Email Provider Using Probability Density Function (306445)

*Ana H Valentin, Marymount University  

Keywords: Poisson, Poisson-Gamma distribution, data loss prevention, personal identifiable information, regulatory expressions

Data protection is an object of serious attention by enterprise security personnel to protect personally identifiable information in compliance with privacy laws from will potential serious consequences of noncompliance through an email provider. This study examines whether the Poisson and Gamma-Poisson Distributions explain the use and value of regular expressions to detect personally identifiable information violation attempt to prevent data loss.

The study describes the occurrences of personally identifiable information attempt violations when regular expressions were updated to neither diminishes nor increases the chance of another incidence and Poisson-Gamma model simultaneously describes the personally identifiable information attempt violation occurrence and intensity at once and a suitable model for zero inflated data which reduces personally identifiable information violation. attempts. The results of the study provide a quantitative methodology for organizations to use regular expressions and Gamma-Poisson Distribution for controlling the incidence probability of personally identifiable information attempt violations on email providers over a period of time.