Derivative Data Security using Artificial Intelligence

Recorded Material: Please click here to view the recorded technical talk.

On Thursday, February 18, 2021 at 6:00 p.m., IEEE Computer Chapter is hosting the technical talk “Derivative Data Security using Artificial Intelligence”.

Day & Time: Thursday, February 18, 2021
6:00 p.m. – 9:00 p.m.

Speaker: Zia Babar

Organizer: IEEE Toronto Computer Chapter

Location: Since this will be a virtual event we will relay the connectivity information later to individual registrants on their email addresses.

Contact: Younas Abbas

Abstract:

Data security the most dynamic and ever evolving trade becomes even significant while dealing with large volumes of unstructured data. To comply with regulation and standards like GDPR it is important to understand, equip and keep abreast of new tools and techniques in data security.

Enterprises are increasingly storing large volumes of unstructured data. However, irrespective of the data format or type, unstructured data is difficult to secure and control its transfer. This is a major problem due to evolving compliance policies and the need to adhere to standards such as GDPR. Through derivative data security practices, enterprises can utilize machine learning and deep learning techniques to determine and trace clones and derivatives of unstructured data across the enterprise. In this talk, Zia Babar will provide a background on data security approaches, and provide a demonstration on machine learning and deep learning techniques can be used for providing derivative data security.

Register: Please visit https://events.vtools.ieee.org/m/252704 to register.

Biography:

Headshot of Zia Babar.

Zia Babar (https://www.linkedin.com/in/zbabar/) has 20 years of professional industry experience, He has deep expertise in the design, development and deployment of enterprise applications, data engineering platforms and distributed systems, with a particular focus on incorporating machine learning practices and cognitive services into software applications. Zia obtained his PhD from the University of Toronto where his research studies focused on the analysis and design of cognitive systems for enabling enterprise transformation. He is presently the Director of Research and Development at WinMagic. Previously, he worked in companies like Teradata where he developed Teradata’s first ML framework, NCR where he was responsible for designing and developing large-scale data processing systems, and Luminous Networks (acquired by Cisco) where he designed and built distributed systems. He is also presently engaged in a multi-year research engagement with IBM Research Labs and is a startup technical mentor at WeWork Labs. Further, he is the organizer of multiple technology meetup groups in both Toronto and Waterloo, and a frequent speaker at technical events and conferences.