Google Privacy Differential Library Open Sourced! 4 Features You Need To Know.
Google in a recent announcement, revealed that it had open-sourced a version of its Privacy Differential Library. It is the same technology behind many of Google’s core products. I.e. Google Maps, etc.
Differential Privacy is a cryptographic approach to the handling of user data. It enables Google to run deep analytics on millions of datasets without compromising user’s privacy.
Differential Privacy mixes artificially generated white noise with user data making them random and untraceable. Thus preventing any third party or even Google from tracking specific individual behavior through its database.
Why is it a big deal?
Because of being user privacy-centric in nature, differential privacy is the key to a more private and secure internet. Now with this move, Google has empowered small businesses, developers, etc. to develop their products without compromising on user privacy.
The quintessential quality of differential privacy is being able to run deep analytics and machine learning on general patterns rather than specific training datasets.
This enables systems to keep user data private, while still being able to learn and advance from the general user activity. This method of running analytics is also used by Apple, to further their algorithms from the data collected from iPhone users. All this without being able to trace any piece of information to any single data point. Here are 4 features of Differential privacy you need to know.
Data Science operations like; ‘counts, sums, averages, medians, and percentiles’ are supported. This enables data scientists and developers to create and run statistical analytics without invading user privacy.
Thus increasing the trust users put into platforms developed through differential privacy. This can lead to having trickle-down benefits for small businesses when building tech solutions for their communities, schools, etc.
Google has also provided support for its extensible ‘Stochastic Differential Privacy Model Checkr Library’. Google also includes its comprehensive software test suit, to help prevent code errors.
This extensive testing suite, helps developers adjust to the new stack better. This will lead to a higher adaptability rate among the developer community. Thus achieving its goal of increasing the privacy-conscious among developers.
The essential benefit of an open-source is in its utility of being used right off the bat. Keeping this in mind, Google has included a PostgreSQL extension to get you started in the ecosystem.
Query engines are one of the most common and important instruments for data analytics. Thus by providing a Structured Query Language, Google has reduced the time to market for many products.
Google has also integrated the privacy differential library with modular functionalities. These functionalities include; ‘Additional mechanisms, aggregation functions, privacy budget management, etc’. Google with the addition of such functionalities has made it easy for cross-platform development.
This will result in developers from a wide variety of development communities utilizing the Privacy Differential Library. Therefore leading to the higher proliferation of privacy-centric tech solutions.
With the growing number of data hacks and privacy breaches, people have started to lose trust in the system. What Google is trying to do right now, is undoubtedly a good effort to preserve user privacy.
Developing tech solutions like the privacy differential library is money and resource-intensive task. Not all small to medium businesses are equipped to do that.
Thus by providing them with the small caliber of tools, a Silicon Valley giant would use, will go leaps and bounds in terms of protecting user data.