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Advance privacy by “De-Identifying” people

August 1, 2014 --

U.S. Chamber of Commerce Foundation

By Michael Hendrix

It’s been an open question for some time whether personal data could be effectively de-identified. That is, could we handle and analyze mass datasets without exposing individual’s personal information?

Some have contended that the answer is “no.” But a recent paper by Ann Cavoukian and Daniel Castro argues otherwise.

“It is possible to strongly de-identify the data (and thus achieve a high degree of privacy), while at the same time preserve the required level of data quality necessary for data analysis. Maximizing both privacy and data quality enables a shift from a zero-sum paradigm to a positive-sum paradigm, a key principle of Privacy by Design. This doubly-enabling ‘win-win’ strategy avoids unnecessary trade-offs and illustrates that it is often possible to de-identify personal information in a manner that maintains both privacy and data quality.”

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