As Roth notes when he refers to a "mathematical proof," differential privacy doesn't merely try to obfuscate or "anonymize" users' data. That anonymization approach, he argues, tends to fail.

Why do we need differential privacy? Existing anonymization methods have been proven to be open to breaches. With enough specific or unique information, an individual’s identity can be ...

Experts warn, however, that differential privacy is not necessarily a silver bullet to the de-anonymization problem. For one thing, it decreases the accuracy of the data being collected.

Differential privacy was invented to tackle that precise problem. The algorithm, which potentially protects people from online attacks, is designed to deliberately add noise to the numbers.

But while data masking is a first attempt at anonymization, it does not make data sets anonymous. ... With differential privacy, no data (whether masked or not) is sent to an analyst.

A technique to measure the privacy of a crucial data set. In 2020, the US government has a big task: collect data on the country’s 330 million residents while keeping their identities private.

As cases of re-identification and de-anonymization emerge, researchers are exploring new, ... RAPPOR, a new product from Google, apply forms of differential privacy for data sharing.

Enterprises crave personalized data, but protecting privacy is non-negotiable. Anonymizing the data brings limitations. Can differential privacy help?

Two years ago, Stellantis established Mobilisights as a new data-as-a-service business unit. Initially focused on fleets, they are now debuting anonymized data platform ...