The Encrypted LLM Experience enables developers to build their own encrypted LLM providing the flexibility to implement custom functions such as a tailored softmax, while leveraging homomorphic encryption for secure computations on sensitive data.
Enabling encrypted statistical analysis, HEaaN.Stat SDK facilitates collaborative statistics by allowing operations like addition, subtraction, and statistical evaluations directly on encrypted data. The SDK provides robust tools for privacy-preserving data analytics, empowering secure collaboration in industries with stringent data protection requirements.
As AI systems grow more powerful, they rely heavily on real and complex data from critical sectors like healthcare and finance. But with this reliance comes an urgent need for stronger privacy protections. FHE-CKKS redefines what’s possible, offering approximate homomorphic encryption specifically designed for real and complex numbers. This enables AI models to process encrypted data with precision, ensuring security remain uncompromised. By embedding privacy directly into the data processing pipeline, CKKS unlocks AI’s full potential without exposing sensitive information. In this new era,
privacy isn’t just a feature—it’s the foundation of innovation.The power of the CKKS scheme lies in its groundbreaking ability to perform secure computations on real and complex numbers, positioning it as the definitive solution for privacy-preserving data processing in the age of AI—a distinction that sets it apart from all other homomorphic encryption schemes.
CKKS enables secure computations on real and complex numbers, a critical feature for modern AI systems, especially in applications that require floating-point arithmetic. Unlike traditional FHE schemes that focus on integer arithmetic, CKKS handles approximate arithmetic, allowing AI models to perform tasks on encrypted real-world data without sacrificing privacy or functionality.
CKKS enables computations on encrypted data through homomorphic operations. It supports various functions, including addition, rotation, conjugation, and multiplication. However, as multiplications are repeated, the ciphertext's level decreases, limiting the depth of computation. To resolve this, CKKS uses bootstrapping to restore the level of the ciphertext, allowing further operations from the encryption level and effectively extending the circuit's lifespan indefinitely with minimal precision loss.
The security of CKKS is based on the hardness of the Ring Learning With Errors problem (RLWE), a difficult computational problem that makes breaking the encryption infeasible. This security framework is particularly significant in the context of post-quantum cryptography (PQC), ensuring that CKKS remains secure against potential attacks from quantum computers, thereby supporting privacy-preserving applications in a future where quantum computing is prevalent.
At the heart of FHE-CKKS is a global movement to rethink how we approach privacy in the age of AI. We believe that true innovation happens when brilliant minds collaborate, share, and push boundaries together. While we’re just beginning to shape this community, you can join us in a dedicated space for discussions, insights, and contributions.
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