With Android 12, Google introduced many new privacy and security settings. It comprises new toggles to enable or disable the camera and microphone. It also indicates in order to show when the camera and microphone are in use. Although, the company only provides a brief introduction to its Android’s new Private Compute Core.
Reportedly, at its I/O developer conference conducted last year. Google make it aware that the Private Compute Core was a secure partition for processing sensitive user data on a device. It is very similar to the one that is used for passwords and biometric data. While, the company also reveals that it is used for several AI-driven features like Live Caption, Now Playing, and Smart Reply. However, it doesn’t reveal its working.
This makes us assume that it uses an Android VM dubbed “microdroid” till Google itself shared a few insights late by last year. Whereby, it revealed that the Private Compute Core was not related to running virtual machines. On the other hand, Google is now finally offering an official statement that gives clarification about Android’s Private Compute Core and how it works.
In a new blog post, Google explains that the Private Compute Core (PCC) “is a secure, isolated data processing environment inside of the Android operating system that gives you control of the data inside, such as deciding if, how, and when it is shared with others. This way, PCC can enable features like Live Translate without sharing continuous sensing data with service providers, including Google. PCC is part of Protected Computing, a toolkit of technologies that transforms how, when, and where data is processed to technically ensure its privacy and safety.”
How Android’s Private Computer Core works
In order to achieve various new functionalities, Google uses techniques like Interprocess Communications (IPC) binds and isolated processes. Also, these techniques are included in Android Open Source Projects and can be controlled by publicly available surfaces like Android framework APIs.
Furthermore, Since, Google has consistently updated its AI features. The same requires machine learning models that run within the PCC up to date. Here, data confidentiality is also important. So Google leverages federated learning and analytics and monitors network calls in order to achieve performance.
To make things further transparent, Google has also published a whitepaper detailing the data protections which make PCC data confidential. It explains its processes and mechanisms, also diagrams of the privacy structure for continuous sensing features. followed by recently revealed open-sourced Private Computing Services.
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