If your data can bring profits, would you still be willing to authorize access to your data?
We all know that AI requires large models trained on data; AI “understands” you through your data, but you don’t know who uses this data, how it circulates, or whether it may be sold to third parties. Therefore, we naturally carry uncertainty and defensiveness regarding data authorization.
- 78% of users believe that AI accessing data violates privacy (Pew Research 2024 data).
- 91% of companies have low AI model accuracy due to data scarcity (MIT report).
This contradiction has given rise to a new era proposition: How to maintain the “sovereignty boundary” of data while enjoying the convenience of AI?
If there were a technology that allows AI to compute without “seeing” the data while adhering to the following three iron rules, I would be willing to authorize it!
1. Transparency of data usage
- Right to know: Clearly inform the purpose of data usage (e.g., “used to train diabetes prediction models” instead of vague “improve service quality”);
- Traceable: Blockchain records each data call, allowing real-time tracking like express logistics.
2. Control completely belongs to the user
- Dynamic authorization: Permissions for specific scenarios can be turned off at any time (e.g., allow analysis of consumption habits, but prohibit use for insurance pricing).
- Gradient opening: Choose the data opening precision as needed (e.g., provide “monthly exercise frequency” to a fitness app, rather than precise heartbeats per minute).
3. Value feedback mechanism
- Contribution equals revenue: When data is used in a business model, earnings are shared based on usage frequency (e.g., medical data training new drug development AI, patients receive sales commissions);
- Tokenized incentives: Instant settlement through tokens like $FHE, avoiding platform delays in “data wages”.
All of the above can be achieved with fully homomorphic encryption (FHE)! It is known as the “Holy Grail of privacy computing” because it realizes the ultimate form of “data usable but invisible”.
👉 The fatal flaw of traditional encryption
- Transmission encryption (SSL): Data still needs to be decrypted for processing after reaching the server, similar to locking confidential documents in a safe for transport, but the recipient must open the box to read it.
- Storage encryption (AES): Data must be decrypted to memory for use, like storing gold in a bank vault, but each time it is used, it must be weighed.
👉 Disruptive breakthrough of FHE
(Imagine you have a magic black box)
- Input: Encrypt the raw data (e.g., medical reports) before placing it in the black box.
- Computation: AI performs analysis directly within the black box (e.g., diagnosing tumor risk), with no access to the content throughout the process.
- Output: Return encrypted results, only you possess the decryption key.
Through which technologies is this achieved:
- Polynomial encryption: Through mathematical transformations, operations like addition, subtraction, multiplication, and division can be performed equivalently on ciphertext.
- Noise control: Using bootstrapping technology to “refresh” ciphertext, preventing cumulative errors from corrupting data.
- Hardware acceleration: NVIDIA H100 GPU can enhance FHE computation efficiency by 100 times, making AI models practical.
3. Compared to other privacy technologies, FHE is more suitable for zero-trust environments than ZKP, MPC, and TEE, supporting complex computations (e.g., AI training).
The maturity of FHE technology marks the first time humanity truly possesses “data doppelgänger technology” — we can create an encrypted mirror that works on our behalf in the digital world to labor, trade, and create value, while our real self remains.
This is not a rejection of AI, but a more advanced symbiosis: let technology respect the boundaries of privacy, allowing data flow to release value.