Data Science Network for the Federal Public Service (DSNFPS)

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Recent articles

Identifying Personal Identifiable Information (PII) in Unstructured Data with Microsoft Presidio

Topics covered in this article: Ethics and responsible machine learning

Identifying Personal Identifiable Information (PII) in Unstructured Data with Microsoft Presidio

In today’s digital age, organizations collect and store vast amounts of data about their customers, employees, and partners. This data often contains Personal Identifiable Information (PII). With the growing prevalence of data breaches and cyber attacks, protecting PII has become a critical concern for businesses and government agencies alike. In this article, Statistics Canada will take a detailed look at Microsoft Presidio and how it helps organizations in Canada comply with privacy laws.

Continue reading: Identifying Personal Identifiable Information (PII) in Unstructured Data with Microsoft Presidio


Introduction to Privacy Enhancing Cryptographic Techniques: Secure Multiparty Computation

Topics covered in this article: Ethics and responsible machine learning

The increasing prevalence of technologies, such as cloud, mobile computing, machine learning (ML), and the Internet of Things (IoT), create opportunities for innovation and information sharing, but also create challenges for data security and privacy. These challenges have been amplified during the global pandemic, working from home has driven faster adoption of hybrid and cloud services. This situation has strained existing security capabilities and exposed gaps in data security (Lowans, 2020). Meanwhile, global data protection legislation is maturing, and every organization that processes personal data faces higher levels of privacy and non-compliance risks than ever before (Wonham, Fritsch, Xu, de Boer, & Krikken, 2020). As a result, privacy-enhanced computation techniques, which protect data while it is being used, have been gaining popularity.

Continue reading: Introduction to Privacy Enhancing Cryptographic Techniques: Secure Multiparty Computation


Computer vision models: seed classification project

Topics covered in this article: Computer Vision

By collaborating with members from inter-departmental branches of government, the Artificial Intelligence Lab team at the Canadian Food Inspection Agency leverages state-of-the-art machine learning algorithms to provide data-driven solutions to real-world problems and drive positive change.

Continue reading: Computer vision models: seed classification project: Indigenous Communities Food Receipts Crowdsourcing with Optical Character Recognition


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