The DSC Seminar seeks to provide a forum for students, staff and faculty to present research ideas and/or results and to encourage conversations about data analysis. The DSC Seminar typically meets every other Friday.

Next Meeting: September 1, 2017 (This Friday) at 3 PM to 4 PM
Location: Shelby Hall, Room 2121

Title: Data Mining and Digital Forensics

Speakers: Brad Glisson, Ryan Benton, and others.


In July, the University of South Alabama (USA) became a founding member of the Center for Advanced Research in Forensic Science (CARFS); CARFS is a National Science Foundation Industry/University Collaborative Research Center. USA leads CARFS’s digital forensics research focus.

The System Protection and Exploitation Research Group (SPERG) will kick off our weekly meetings Friday, 25 August. We will be meeting 10:00AM-11:00AM every Friday in SHEC 2327. Everyone is welcome to attend and we normally have a full house!

The Digital Forensics Information Intelligence (DFII) Research Group consist of academic faculty from computing science, information systems and information technology, undergraduate and post graduate students, along with industry professionals.

Meeting Time: 5:15 to 6:00 pm every Wednesday

Location: Shelby Hall: Room 2119

The speaker this week is Mrs. Maureen Van Devender.

The title of the talk: Understanding De-identification of Healthcare Big Data

Abstract: In society’s increasingly computerized world, the intensification of electronic data collection is resulting in large volumes of new data (known as big data). This is creating new opportunities for secondary uses of this data, particularly in the healthcare sector. The opportunities for secondary uses of healthcare data include constructive studies, research, policy assessment and other endeavors that could produce beneficial results such as improved healthcare quality and finding cures for diseases. However, protecting the privacy of individuals represented in data presents a challenge to the secondary utility of healthcare data. De-identifying data by removing any information that could be used to uniquely identify individuals is a potential solution to the challenge of protecting individual privacy. Hence, this research identifies a practical process for applying anonymizing techniques through a process model designed to handle requests for healthcare data.

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