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#News(Security) [ via IoTGroup ]
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/i>Whether you’re a city planner, a small business owner, or a software developer, gaining useful insights from data can help make services work better and answer important questions.
Differential privacy is a high-assurance, analytic means of ensuring that use cases like this are addressed in a privacy-preserving manner.
Today, we’re rolling out the open-source version of the differential privacy library that helps power some of Google’s core products.
To make the library easy for developers to use, we’re focusing on features that can be particularly difficult to execute from scratch, like automatically calculating bounds on user contributions.
It is now freely available to any organization or developer that wants to use it.
Our open source library was designed to meet the needs of developers.
Rigorous testing: Getting differential privacy right is challenging.
Besides an extensive test suite, we’ve included an extensible ‘Stochastic Differential Privacy Model Checker library’ to help prevent mistakes.
Getting differential privacy right is challenging.
Besides an extensive test suite, we’ve included an extensible ‘Stochastic Differential Privacy Model Checker library’ to help prevent mistakes.
Ready to use: The real utility of an open-source release is in answering the question “Can I use this?” That’s why we’ve included a PostgreSQL extension along with common recipes to get you started.
The real utility of an open-source release is in answering the question “Can I use this?” That’s why we’ve included a PostgreSQL extension along with common recipes to get you started.
Modular: We designed the library so that it can be extended to include other functionalities such as additional mechanisms, aggregation functions, or privacy budget management.
We have driven the research and development of practical, differentially-private techniques since we released RAPPOR to help improve Chrome in 2014, and continue to spearhead their real-world applica
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