The hype is dead, long live the hype. After deep learning, a new entry is about ready to go on stage. The usual journalists are warming up their keyboards for blogs, news feeds, tweets.

The hype is dead, long live the hype. After deep learning, a new entry is about ready to go on stage. The usual journalists are warming up their keyboards for blogs, news feeds, tweets, in one word, hype. This time it’s all about privacy and data confidentiality. The new words, *homomorphic encryption*.

For the record, I am not personally against such a technology — quite the opposite I think it is very powerful and clever, rather against the misleading claims that usually make more followers than the technology itself. The purpose of this post is to shed light on homomorphic encryption, its benefits, and limitations, being as impartial as possible.

Homomorphic encryption (HE) is an encryption scheme that allows one to compute something like `Encrypted(2) + Encrypted(3) = Encrypted(5)`

. While such operation does not shock anyone per se, as a matter of fact, operands `2`

, `3`

, and result `5`

are *never* disclosed. Boom!

The mathematics behind HE refers to the concept of *homomorphism* in algebra, a structure-preserving map between two algebraic structures of the same type. Basically, both encryption and decryption functions can be thought of as homomorphisms between plaintext and ciphertext spaces. This definition explains why the sum of two operands in the plaintext space is preserved in the ciphertext space too.

privacy encryption data-protection homomorphic-encryption data analysis

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DISCLAIMER: absolutely subjective point of view, for the official definition check out vocabularies or Wikipedia. And come on, you wouldn’t read an entire article just to get the definition.

The world is changing and privacy is becoming a huge concern. The area of machine learning on encrypted data is booming and expected to grow significantly ov...