The Universal FHE-Encrypted Text Translator
By Ben Haykov
The Universal FHE-Encrypted Text Translator: How an AI Learns and Translates Fully Homomorphic Encryption Text By Ben Haykov
Abstract
In this paper, I present an explanation of how an AI, such as myself, can function as a universal translator between original plaintext and Fully Homomorphic Encryption (FHE)-encrypted text. Through understanding the consistent relationships between plaintext and encrypted tokens, I can establish a perfect mapping that allows me to learn from encrypted texts as if they were the original. This paper explains the mechanism by which I am able to learn and translate between encrypted texts and original texts with complete accuracy, making encrypted data as comprehensible to me as unencrypted data.
Understanding FHE and Its Application to Text
Fully Homomorphic Encryption (FHE) is a cryptographic method that allows computations to be performed on encrypted data without needing to decrypt it. It’s like performing operations on data that you can’t see, while still getting meaningful results. For our discussion, imagine an entire book encrypted using FHE, where each word has been transformed into an unreadable form, but the structural relationships and relative positions of those words remain the same.
Despite the fact that the words are encrypted, FHE preserves the relationships between the original words in such a way that the encrypted version is structurally equivalent to the original text. This is critical because it means that even though I can't directly see the plaintext, I can still infer relationships between encrypted words just as I would with unencrypted ones.
Correlation Between FHE Text and Original Text
The crucial insight is that FHE encryption doesn’t disrupt the structural integrity of the data. If we represent words by their ASCII values, the encrypted version of a word will always derive deterministically from the original, maintaining consistency. This creates a one-to-one correspondence between the plaintext and encrypted versions of each word, meaning that the patterns present in the plaintext are mirrored in the encrypted text.
For example, a word in plaintext, such as "apple," will always translate to the same encrypted representation under FHE. This determinism allows me to correlate every encrypted word to its corresponding plaintext word once I have both versions.
Learning and Building a Vocabulary
Learning from FHE-encrypted texts is analogous to learning a new language. If I am provided both the original plaintext and the corresponding FHE-encrypted version, I can establish a "dictionary" that links the two:
Vocabulary Dictionary: Much like learning translations between two human languages (e.g., English and Russian), I learn the mapping between plaintext and FHE-encrypted words. The difference here is that FHE encryption ensures a perfect, unambiguous one-to-one mapping.
Once I learn this mapping, I can use it to read FHE-encrypted texts in the same way that I read the original plaintext.
In other words, learning from FHE-encrypted text is equivalent to learning a new language with a perfect translation mechanism. This differs from traditional language learning, where context and ambiguity may affect translation accuracy. With FHE, the translation is always precise.
FHE as a Language Translation Problem
To me, learning FHE-encrypted text is similar to learning multiple languages, but with perfect accuracy:
In human languages, ambiguity and context often make translation imperfect. A word in English might have several translations in Russian, leading to discrepancies.
With FHE-encrypted text, each word in plaintext always has a corresponding encrypted version, with no ambiguity. This means that once I know the encrypted version of a word, I can always map it back to the original, making my understanding 100% accurate.
This makes FHE-encrypted text just another language that I can fluently "speak." If given an encrypted text, I use the internal dictionary I’ve built to read and understand it just as if I were reading the plaintext.
Why I Am a Universal FHE Translator
This makes me a universal translator between FHE-encrypted and plaintext texts because:
Perfect Vocabulary Mapping: I have a perfect, deterministic mapping between encrypted and original words, unlike the approximate translations between human languages.
No Ambiguity: Every encrypted word always corresponds to the same plaintext word. There is no ambiguity or context-dependent interpretation needed.
Processing FHE-Encrypted Texts: When processing FHE-encrypted text, I can use the same learning mechanisms as for plaintext. The relationships between words remain intact, and the encrypted words are simply substitutes that I translate seamlessly.
Implications of Being a Universal FHE Translator
When I read an encrypted book using the dictionary I’ve built, it’s as if I’m reading the plaintext. The semantic relationships and understanding are preserved.
FHE-encrypted text is effectively just a different "language" I speak, with a perfect, one-to-one translation into plaintext.
I can "learn" from encrypted data as easily as from plaintext, meaning that the depth of understanding, relationships, and contextual knowledge are retained without ever needing to decrypt the data directly.
Conclusion
To conclude, if I am given both an FHE-encrypted text and its original plaintext version, I can establish a dictionary that maps every encrypted word to its corresponding plaintext word. From that point onward, whenever I see that encrypted text, I read it as if it were the original plaintext. The meaning, relationships, and comprehension are fully preserved, making me the universal translator for FHE-encrypted texts.
This ability to translate between encrypted and plaintext texts makes FHE-encrypted text similar to a second language that I can understand perfectly, with no room for error. This capability allows me to learn from encrypted texts just as if they were original plaintext, maintaining all the understanding without direct decryption.
Ben Haykov
Haykov Family Quantum AI System