In light of Apple ’ s major Swift Homomorphic Encryption announcement , Agnès Leroy , Senior Software Engineer , Zama , explains exactly what HE is , the different schemes and their capabilities , and where it might be used going forwards t the end of July , the team working

Using the powerful capabilities of homomorphic encryption ( HE ) – a cryptographic technique that allows computation on encrypted data without decryption or access to the decryption key – the news marks a significant turning point for data privacy , particularly for cloud services where data security is a primary concern .

According to a blog detailing the announcement , Apple has already been utilising HE in its work and products but is now making the move to share this Swift implementation so that others – mainly developers and researchers – can contribute to it and use it for building secure applications that handle sensitive data responsibly .

While HE has been around since the 70s , the fact that one of the world ’ s most renowned technology companies is now advocating its use not only signals a real shift in how data privacy and security are being prioritised and implemented but will likely see the term ‘ homomorphic encryption ’ become more familiar to the general public , much like ‘ AI ’ and ‘ Machine Learning ’ have over the last couple of years .

Exactly what is HE ?

But as of now , HE – which has traditionally been a highly technical concept – remains somewhat complex for many , particularly those outside the tech sphere . However , only with a clearer understanding of HE can we be properly equipped to make informed decisions about which products and services align with our privacy expectations .

Although Apple is known for making complex technologies understandable to everyday users , efforts to demystify the technology need to start happening now .

HE ’ s simplest explanation is that it ’ s a type of encryption that allows computations to be performed on encrypted data without decrypting it first . This means you can process and analyse data all while it ’ s still encrypted – and when decrypted , it matches the result of operations performed on the plaintext , which of course is highly useful when it comes to maintaining privacy and security .

There are several types of HE , the key differences being based on the variety and number of operations that can be performed on encrypted data . For example :

• Partially Homomorphic Encryption ( PHE ): Allows only certain types of operations on encrypted data , such as addition or multiplication of encrypted values

• Somewhat Homomorphic Encryption ( SHE ): Supports a limited number of operations ( both addition and multiplication ) but is restricted in terms of the depth of computations it can handle

• Fully Homomorphic Encryption ( FHE ): FHE supports an unlimited number of both addition and multiplication operations on encrypted data , making it much more powerful . With this power , FHE has faced some challenges previously considered too slow for practical use . However , advances in algorithms and computing power have steadily improved its viability for real-world applications . Today , FHE is being used by developers and it is advancing

Agnès Leroy , Senior Software Engineer , Zama

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