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How Machine Learning & Differential Privacy Can Be Used to Anonymize Production Data
Governments, policymakers and international organizations are under considerable pressure to protect personally identifiable information (PII). As regulation increases, it’s even more important to anonymize internal data used by development teams to protect your organization from PII leaks.
To date, development teams have often tested against production data, as it is believed to be the most realistic and can catch issues before market release In this white paper, our experts discuss a safer alternative for development teams to consider that uses machine learning with a data set that is trained to generate an anonymized data set from client production data. This approach retains the utility of the data without the inherent risk.
This paper first breaks down anonymization and the challenges in generating a data set that’s as useful as the original production data. From there, we use publicly available data sets as an example and compare the resulting anonymized data set against the original data to highlight our methodology. Download the paper to learn more about EPAM’s effective approach.
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In the News
Data Paradox Meaning and 101 Introduction
In this Solutions Review article, EPAM CISO, Sam Rehman, explains the greater demand for raw data and pressing need for security and privacy.
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In the News
More Data isn’t the Problem – It’s Less Security
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Case Study
Driving Digital Courseware, Education Technology & Assessment for a Leading Learning Company
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Podcast
Care About Your Customer: The Best Strategy for Travel Brands in the New Normal
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Video
EPAM’s Connectivity Cockpit
EPAM’s Connectivity Cockpit collates data from all input sources from user touchpoints to customer support and turns the information into predictive actionable insights.