Ensuring data privacy and security during computational processes presents a significant challenge, particularly when using cloud services. Traditional encryption methods require data to be decrypted before processing, which exposes it to potential risks. Homomorphic encryption offers a promising solution, as it allows calculations to be performed on encrypted data without revealing the underlying information.
Apple is introducing a new open-source Swift package, swift-homomorphic-encryption, to implement this cryptographic technique. This package allows computations to be performed on encrypted data without decrypting it or accessing the decryption key. Clients can send encrypted data to a server, which processes it and returns an encrypted result that the client can decrypt. This approach keeps data private and secure throughout the computational process, making it ideal for cloud services.
The swift-homomorphic-encryption package takes advantage of several advanced features:
- Swift on the server: using the Hummingbird HTTP framework and cross-platform support.
- Reference Library: For easy performance benchmarking.
- Swift crypto: Provides high-performance low-level cryptography primitives.
The implementation uses the Brakerski-Fan-Vercauteren (BFV) HE scheme, based on the Ring Learning Hardness with Errors (RLWE) problem. This ensures 128-bit post-quantum security, allowing secure computations on encrypted data and providing protection against both classical and future quantum attacks.
Apple uses homomorphic encryption in the Live Caller ID Lookup feature in iOS 18. This feature provides caller ID and spam blocking services by sending an encrypted query to a server that retrieves information about a phone number without knowing the specific phone number in the request. The live-caller-id-lookup-example backend demonstrates this functionality, highlighting the practical application of homomorphic encryption.
The live caller ID lookup feature is also based on Private Information Retrieval (PIR), which allows clients to perform private key-value database lookups. In the PIR setting, a client has a private keyword (such as a phone number) and wants to retrieve the associated value from a server. Because the keyword is private, the client wants to perform this lookup without the server knowing it. This implementation uses homomorphic encryption, which requires only a small amount of database metadata to be synchronized with the client. This efficient approach supports very large databases with frequent updates, improving data privacy and security.
A basic usage example of the Swift homomorphic encryption suite demonstrates the workflow:
In conclusion, the swift-homomorphic-encryption package enables developers and researchers to build privacy-preserving applications inside and outside the Apple ecosystem. Potential applications include private set intersection, secure aggregation, and machine learning. The community is encouraged to contribute to the project and explore new use cases for homomorphic encryption, fostering innovation and improving data security.
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Sana Hassan, a Consulting Intern at Marktechpost and a dual degree student at IIT Madras, is passionate about applying technology and ai to address real-world challenges. With a keen interest in solving practical problems, she brings a fresh perspective to the intersection of ai and real-life solutions.
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