Top 5 Drawbacks to Using Only Data Masking
Although data masking and de-identification are often grouped together for discussion, the two use different approaches in making data anonymous. Furthermore, masking and de-identification deal with different identifiers in a dataset. Masking is used to anonymize direct identifiers while de-identification is used to anonymize quasi-identifiers. In practice, masking and de-identification should be used together to optimize the balance between protecting privacy and maintaining the usefulness of the data. This paper explores the major limitations of using data masking on its own, without de-identification.
In this white paper, learn exactly what the top 5 drawbacks are to using only data masking and why they need to be avoided.
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.
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