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De-Identification 201: The Basics of Data De-identification
Only a handful of experts exist around the world who are qualified to manually de-identify data. This is because de-identification is a complex and challenging field that requires highly specific knowledge. Simply removing the names and other types of direct identifiers from a dataset is insufficient to achieve de-identification. The data will also contain other indirect or quasi-identifiers, such as age, date of birth and zip code that, when combined, can be used to positively identify an individual.
As would-be attackers get smarter, data de-identification strategies become more sophisticated. It can be hard to keep up. Learning the basics helps you be part of the discussion.
Join us for De-identification 201, Fundamentals of Data De-identification.
This whitepaper covers classic de-identification techniques like record suppression, cell suppression, sub-sampling and aggregation as well as the pros and cons of Safe Harbour and Expert De-identification strategies.
Make sure to read De-identification 101, 301 and 401 to get the full picture.
Situation: California’s Consumer Privacy Act inspired Comcast to evolve the way in which they protect the privacy of customers who consent to share personal information with them.
Situation: Integrate.ai’s AI-powered tech helps clients improve their online experience by sharing signals about website visitor intent. They wanted to ensure privacy remained fully protected within the machine learning / AI context that produces these signals.
Situation: Novartis’ digital transformation in drug R&D drives their need to maximize value from vast stores of clinical study data for critical internal research enabled by their data42 platform.
Situation: CancerLinQ™, a subsidiary of American Society of Clinical Oncology, is a rapid learning healthcare system that helps oncologists aggregate and analyze data on cancer patients to improve care. To achieve this goal, they must de-identify patient data provided by subscribing practices across the U.S.
Situation: Needed to ensure the primary market research process was fully compliant with internal policies and regulations such as GDPR.
Situation: Needed to enable AI-driven product innovation with a defensible governance program for the safe and responsible use
of voice-to-text data under Shrems II.
This course runs on the 2nd Wednesday of every month, at 11 a.m. ET (45 mins). Click the button to register and select the date that works best for you.