What is homorphic encryption?
Generated on 8/1/2024
1 search
Homomorphic encryption is a form of encryption that allows computations to be performed on encrypted data without needing to decrypt it first. This means that a server can process encrypted data and return an encrypted result, which can then be decrypted by the client to reveal the final output. This technique is particularly useful for maintaining privacy and security, as it ensures that sensitive data remains encrypted throughout the entire process.
For example, in the context of Apple's WWDC, homomorphic encryption is used in the implementation of Live Caller ID. This allows a server to evaluate an encrypted incoming phone number for a match without ever decrypting it, thereby preserving the privacy of the phone number. The server computes on the incoming ciphertext, evaluates for a match, and then returns the transformed ciphertext back to the requesting device, where the results are displayed.
For more details, you can refer to the session What’s new in privacy at the 21:49 mark.
What’s new in privacy
At Apple, we believe privacy is a fundamental human right. Learn about new and improved permission flows and other features that manage data in a privacy-preserving way, so that you can focus on creating great app experiences.
Platforms State of the Union
Discover the newest advancements on Apple platforms.
Deploy machine learning and AI models on-device with Core ML
Learn new ways to optimize speed and memory performance when you convert and run machine learning and AI models through Core ML. We’ll cover new options for model representations, performance insights, execution, and model stitching which can be used together to create compelling and private on-device experiences.
Bring your machine learning and AI models to Apple silicon
Learn how to optimize your machine learning and AI models to leverage the power of Apple silicon. Review model conversion workflows to prepare your models for on-device deployment. Understand model compression techniques that are compatible with Apple silicon, and at what stages in your model deployment workflow you can apply them. We’ll also explore the tradeoffs between storage size, latency, power usage and accuracy.
Accelerate machine learning with Metal
Learn how to accelerate your machine learning transformer models with new features in Metal Performance Shaders Graph. We’ll also cover how to improve your model’s compute bandwidth and quality, and visualize it in the all new MPSGraph viewer.
What’s new in Swift
Join us for an update on Swift. We’ll briefly go through a history of Swift over the past decade, and show you how the community has grown through workgroups, expanded the package ecosystem, and increased platform support. We’ll introduce you to a new language mode that achieves data-race safety by default, and a language subset that lets you run Swift on highly constrained systems. We’ll also explore some language updates including noncopyable types, typed throws, and improved C++ interoperability.
Go small with Embedded Swift
Embedded Swift brings the safety and expressivity of Swift to constrained environments. Explore how Embedded Swift runs on a variety of microcontrollers through a demonstration using an off-the-shelf Matter device. Learn how the Embedded Swift subset packs the benefits of Swift into a tiny footprint with no runtime, and discover plenty of resources to start your own Embedded Swift adventure.
Explore Swift performance
Discover how Swift balances abstraction and performance. Learn what elements of performance to consider and how the Swift optimizer affects them. Explore the different features of Swift and how they’re implemented to further understand the tradeoffs available that can impact performance.