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Integrating Data-Privacy Through Pipelines

All data stored on a filesystem has some metadata. Sometimes more and other times less. This can be a huge privacy breach, since the metadata can contain sensible data that can be used to identify persons, locations, or other interesting information.
To not leak any hidden sensitive information, it is crucial to ensure that all data that is stored and processed is clean. This task is predestined to automate.

This talk will focus on how to remove all the metadata and automate this procedure through data processing pipelines that can be used in an MLOps as well as the classical DevOps cycle.

Vorkenntnisse

Fundamental Machine Learning and Data Science terms and practices

Lernziele

Automation of removal of (sensitive) metadata in a wide variety of areas

Speaker

Mamoona Aslam works as a Cloud Architect at ndaal where she designs Cloud strategies and automate System Operations. With her background in Cryptography and Data Security, She currently provides consulting in the areas of DevOps/MLOps, Cloud Infrastructure, and IT Compliance in several projects.

Ayesha Shafqat works as a Data Scientist and DevSecOps Automation Engineer for cloud security at ndaal. Her focus is on secure end-to-end infrastructure automation on several platforms that generate a significant amount of system and user data to ensure that AI automation projects run successfully.

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