Today, I will talk to you about the Mind Network project and how it uses 'Fully Homomorphic Encryption' (FHE) to change the future of AI — just like putting an invisible cloak on internet data while allowing AI to dance with shackles!
Regarding the previously popular DeepSeek, Mind Network is the first FHE project integrated with DeepSeek.
1. First, complain about the current situation: the 'naked running' crisis of AI.
Today's large models (like ChatGPT) require massive amounts of data during training — your chat history, medical reports, financial information may all have been fed in. But the problem is:
Data exposure: The raw data on the server may be viewed by hackers (imagine the leakage of hospital records).
To use data, it must be stripped bare: traditional encrypted data must be decrypted to compute (like having to empty the money from a safe to count it).
This is not safe! 😱
2. What is Mind Network doing?
In simple terms: they used 'Fully Homomorphic Encryption' (FHE) to put data in an 'invisible cloak', allowing AI to process data directly in encrypted form without seeing the original content.
Your data → Encrypted into a pile of garbled text → AI computes based on the garbled text → Outputs encrypted results → Only you can decrypt.
Throughout the process, the data is locked in a safe, but AI can still help you count the money!
3. What is the black technology of FHE?
The brilliance of Fully Homomorphic Encryption (FHE):
Encrypted data can also be computed: for example, 'encrypted 5' + 'encrypted 3' = 'encrypted 8' (but outsiders see only garbled text).
Mind Network's optimization: They use blockchain + FHE to solve the slow speed of traditional FHE (what used to take an hour for addition may now only take a few seconds).
Analogy: Previously, AI had to 'open the envelope to read the letter', now it can 'feel the words through the envelope' and still understand! ✉️🔒
4. How does this 'reshape the future of AI'?
Explosion-level privacy protection:
Hospitals can share encrypted medical records to train AI, but no one can see specific patient information.
Your chat history won't be spied on by AI companies.
Data monopoly is broken:
Small companies can safely use encrypted data from large companies (for example, training models with encrypted Twitter data).
The true integration of Web3 + AI:
Smart contracts on the blockchain can handle encrypted data (such as automatically processing encrypted insurance data).
The effect is: AI gets plenty of data without invading privacy. 🎯
5. For example 🌰
Imagine you are a doctor who wants to use AI to analyze a patient's encrypted CT scan:
Traditional method: You have to decrypt the CT image first → Upload it to the AI server (which may leak) → Analyze.
Mind Network's solution:
Your CT image is locally encrypted into an 'alien code'.
AI analyzes directly on the encrypted data and outputs 'encrypted diagnostic results'.
Only you can decrypt with the key and see the result is 'benign tumor'.
The server doesn't know whether the image is CT or X-ray throughout the process!
6. Challenges and Complaints
Of course, FHE is still a bit 'clumsy' now:
Slow computation: Although Mind Network has accelerated with parallel computing, it is still slower than plaintext computation (like running while wearing a gas mask).
Requires hardware support: They may rely on dedicated chips (such as NVIDIA's FHE accelerators).
However, if successful, this will be the 'privacy condom' of the AI era! 🚀
Summary: Mind Network = The encryption bodyguard of AI.
Transforming data into 'riddles', letting AI learn to 'guess riddles', and in the end, only you can understand the answer — protecting privacy while releasing data value. If successful, AI will no longer be criticized for 'stealing data' in the future!
