[[{“value”:”
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, exposing it to potential risks. Homomorphic encryption offers a promising solution, allowing computations on encrypted data without revealing the underlying information.
Apple introduces a new open-source Swift package, swift-homomorphic-encryption, to implement this cryptographic technique. This package enables computation on encrypted data without decrypting it or accessing the decryption key. Clients can send encrypted data to a server, which processes the data and returns an encrypted result that the client can decrypt. This approach maintains data privacy and security throughout the computational process, making it ideal for cloud services.
The swift-homomorphic-encryption package leverages several advanced features:
Swift on Server: Utilizing the Hummingbird HTTP framework and cross-platform support.
Benchmark Library: For easy benchmarking of performance.
Swift Crypto: Providing performant low-level cryptography primitives.
The implementation uses the Brakerski-Fan-Vercauteren (BFV) HE scheme, based on the ring learning with errors (RLWE) hardness problem. This ensures post-quantum 128-bit security, enabling secure computation on encrypted data and protecting against both classical and potential future quantum attacks.
Apple utilizes homomorphic encryption in iOS 18’s Live Caller ID Lookup feature. 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 also relies on Private Information Retrieval (PIR), allowing 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 learning the keyword. This implementation uses homomorphic encryption, requiring only a small amount of database metadata to be synchronized with the client. This efficient approach supports very large databases with frequent updates, enhancing data privacy and security.
A basic usage example of the Swift Homomorphic Encryption package demonstrates the workflow:
In conclusion, The swift-homomorphic-encryption package empowers developers and researchers to create privacy-preserving applications within and beyond 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 enhancing data security.
Check out the Details. All credit for this research goes to the researchers of this project. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Group. If you like our work, you will love our newsletter..
Don’t Forget to join our 47k+ ML SubReddit
Find Upcoming AI Webinars here
The post Apple Introduces Homomorphic Encryption via Swift: Revolutionizing Privacy-Preserving Cloud Computations appeared first on MarkTechPost.
“}]] [[{“value”:”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, exposing it to potential risks. Homomorphic encryption offers a promising solution, allowing computations on encrypted data without revealing the underlying information. Apple introduces a new open-source Swift package,
The post Apple Introduces Homomorphic Encryption via Swift: Revolutionizing Privacy-Preserving Cloud Computations appeared first on MarkTechPost.”}]] Read More AI Shorts, Artificial Intelligence, Editors Pick, Security, Staff, Tech News, Technology