Mark your calendar and come join us for CAE Forum! CAE Forum is a live, real-time, online academic forum where members of the CAE community give non-technical presentations on topics of value to the CAE community.

CAE Forum is about sharing your ideas, knowledge, and expertise to empower and strengthen our community. It's that simple. CAE Forum presentations are normally held on the third Wednesday of each month during the fall and spring semesters.

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Presentation 1: Workload Modeling for Privacy and Attack Detection in Databases

Date: Wednesday, March 2, 2022

Time: 1:00-1:50 p.m. ET

Location: 
Just log in as "Guest" and enter your name; no password required.

Audience: Students, professors, government

Presenter(s):  Gokhan Kul, Ph.D., University of Massachusetts Dartmouth 

Description:  Database systems have become an essential component of every data-intensive application. Both academia and industry invest in developing tools and methodologies to protect the privacy of the data stored while satisfying the performance requirements of these systems. We model database workloads to achieve both of these goals by exploiting the ever-evolving behavioral characteristics of users. In this talk, I will first present how traditional database workload modeling is ineffective in terms of performance and security when applied to mobile devices and how mobile databases can benefit from accurate workload modeling. Next, I will introduce a novel and lightweight data-leakage detection system on mobile databases. This system models the evolving behavior by comparing probability distributions of the query workload features over time and uses this model to determine if the incoming query activity is anomalous.

Presentation 2: Advertising Technology (AdTech)

Date: Wednesday, March 2, 2022

Time: 2:00-2:50 pm EST

Location: 
Just log in as "Guest" and enter your name; no password required.

Audience: Students, professors, government

Presenter(s): Dr. Babur Kohy, Northern Virginia Community College

Description: Database systems have become an essential component of every data-intensive application. Both academia and industry invest in developing tools and methodologies to protect the privacy of the data stored while satisfying the performance requirements of these systems. We model database workloads to achieve both of these goals by exploiting the ever-evolving behavioral characteristics of users. In this talk, I will first present how traditional database workload modeling is ineffective in terms of performance and security when applied to mobile devices and how mobile databases can benefit from accurate workload modeling. Next, I will introduce a novel and lightweight data-leakage detection system on mobile databases. This system models the evolving behavior by comparing probability distributions of the query workload features over time and uses this model to determine if the incoming query activity is anomalous.

A  will be available within 48 hours of the presentation.