Based on materials from the site - By PrivaseaAI

At Privasea, our team of researchers and developers is tackling one of the most challenging tasks in the field of artificial intelligence: how to make recommendation systems useful without requiring access to raw user data.
Most modern recommendation systems rely on input data from open text — what you watch, click, or read is collected and analyzed directly. While this provides personalization, it also creates a significant risk of information disclosure. Service providers see everything, and users lose control over their information.
Our team of researchers and developers is working to change this situation. The result is our first international patent for the CURE homomorphic clustering method — a system that performs clustering and issues recommendations directly based on encrypted data using fully homomorphic encryption (FHE).
Instead of decrypting data for analysis, our method allows the clustering process to remain fully encrypted from start to finish. This is achieved by applying the CURE clustering algorithm in the encrypted domain without disclosing plaintext at any stage.
Key research directions:
Development of a separate computing model, where preprocessing is performed locally on the device, and encrypted clustering is done in the cloud.
Implementation of SIMD acceleration to pack thousands of encrypted vectors into a single ciphertext, enhancing performance.
Using encrypted "representative points" to form clusters that capture useful group behavior without disclosing individual data.
This important stage of research and development demonstrates that recommendation systems can remain practical even if the data stays encrypted.
For users, this means personalization without surveillance. For service providers, it removes the responsibility for processing confidential datasets in plaintext.
Translating concepts from the patent into practical application
Although this patent protects the core method, work on this does not stop. Our research program continues:
Optimization of encrypted clustering to reduce latency.
Extending homomorphic methods to more advanced recommendation models.
Researching integration into operational systems, such as content channels and product discovery platforms. This R&D overview is just the first step towards creating a larger platform: artificial intelligence, originally encrypted and inherently trustworthy.
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