It is implemented by combining user data with artificial white noise, as explained by Wired's Andy Greenberg. In this way, the results of any analysis can not be used to unmask an individual, nor can a malicious third party be allowed to track any data point and return it to an identifiable source.
For example, the technology is the cornerstone of Apple's privacy-aware machine learning approach. It allows Apple toUsers extract data from them, process them statistically anonymously, and still provide useful insights that can help it improve its Siri algorithm.
Google has done the same with chrome, using random aggregated privacy protection ordinals to respond to (RAPPOR), a differential privacy tool used to analyze and extract insights from browsers to prevent sensitive information such as personal browsing history from being traced. Earlier this year, Google also opened a tool called Tensorflow Privacy for its Tensorflow artificial intelligence training platform, allowing researchers to use different privacy tools to protect user data while training artificial intelligence algorithms.
But Google points out that there are many other areas, such as health care and sociology, where differential privacy may be useful. This type of analysis can be achieved in a variety of ways and used for many different purposes. Differential privacy is a highly guaranteed analysis tool that can ensure privacy protection while resolving such use cases. Google Search's Open Source Differential Privacy Library on GitHub can help organizations and individuals without the resources of large Silicon Valley technology companies use the same rigorous privacy methods for data analysis.