5 results about "Bootstrapping" patented technology

Some embodiments are directed to a computer-implemented method (1200) of performing a computation on LWE-encrypted values. The invention relates to the TFHE setting in which the encryption supports a programmable bootstrapping operation that evaluates an LWE decryption in an exponent of a GLWE-encrypted monomial. In this setting, a LWE-encrypted product value is computed based on one or more LWE-encrypted input values by applying a cryptographic key switching operation to convert the LWE-encrypted input values to GLWE-encrypted polynomials, wherein a coefficient of an polynomial is a linear combination of the set of input values; applying an encrypted polynomial multiplication in the GLWE domain, and applying a cryptographic sample extraction operation to the resulting GLWE-encrypted polynomial to obtain the LWE-encrypted product value as an encryption of one of its coefficients.

One example method includes, at a node, installing a default parametrization configuration that facilitates performance of a domain task, obtaining, by the node, a distilled dataset, and obtaining the distilled dataset is either: obtaining the distilled dataset from another node; or leveraging a synthetic state assembled in the node to select the distilled dataset from another node based on state similarity of the node to the another node. The example method further includes training a model at the node, and the training is performed using the distilled dataset, and the trained model is operable to leverage information received by the node to propose changes to the parametrization configuration so as to optimize execution of a task by the node.

Methods and systems for performing efficient privacy-preserving machine learning (or other computationally intensive private computing operations) based on homomorphic encryption are disclosed. Two computer systems can collectively perform these methods on behalf of a client computer using threshold leveled homomorphic encryption (LHE) or fully homomorphic encryption (FHE), e.g., using the Cheon-Kim-Kim-Song cryptosystem. For example, two computer systems could privately evaluate a client's encrypted inputs using a machine learning model and return an encrypted output to the client, preserving the privacy of the client's data. Methods according to embodiments include efficient bootstrapping methods, ciphertext packing methods, encryption parameter optimization methods, and methods for privately fine-tuning machine learning models using low rank adaptation (LoRA). Embodiments improve the speed and efficiency of private HE-based computation, which are often several orders of magnitude slower than non-private computation.

A multi-party system comprising a garbler and an evaluator for interactively executing homomorphic SIMD operations using garbled circuits. The garbler and evaluator may each store a unique share of a shared secret key, a ciphertext, and a shared public key. The garbler and evaluator may each partially decrypt the ciphertext using its key share to generate a unique data share. The garbler may linearize and reduce the size of the unique garbler data share. The garbler may send to the evaluator a garbled circuit, a garbling of the linear unique garbled data share, and garbled potential wires for the evaluator to garble its linear unique evaluator data share by oblivious transfer. The evaluator may evaluate the garbled circuit to execute a SIMD program to combine, in parallel, multiple indices of the linear garbler and evaluator unique data shares to efficiently generate an encrypted result of the garbled circuit.

This invention discloses a metadata-driven method and system for constructing a large language model intelligent agent through bootstrapping, relating to the field of artificial intelligence technology. The method includes the following steps: collecting natural language requirements generated from business scenarios and organizing these requirements into a unified expression format; generating readable metadata description content within this unified expression format; performing field splitting and role attribution around the metadata description content; constructing data type definition metadata within the unified expression format; and embedding prompt words into the data type definition metadata to generate content. This invention achieves progressive generation from natural language to data types, rules, and intelligent agent definitions through unified metadata expression, supporting dynamic construction and automatic loading at runtime, reducing manual intervention, and improving automation. Simultaneously, new metadata is generated during runtime and fed back into the generation chain, forming a continuous bootstrapping closed loop, enhancing structural consistency and version evolution controllability.