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Big Data Privacy for Machine Learning Just Got 100 Times Cheaper

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Differential privacy, which is used by more than one tech giant, is based on the idea of adding random noise to obscure individual information.


Rice University computer scientists have discovered an inexpensive way for tech companies to implement a rigorous form of personal data privacy when using or sharing large databases for machine learning.

"There are many cases where machine learning could benefit society if data privacy could be ensured," said Anshumali Shrivastava, an associate professor of computer science at Rice. "There's huge potential for improving medical treatments or finding patterns of discrimination, for example, if we could train machine learning systems to search for patterns in large databases of medical or financial records. Today, that's essentially impossible because data privacy methods do not scale."

From Rice University News
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