Deep learning using large codeword model with homomorphically compressed data
A system and method for deep learning using a large codeword model with homomorphically compressed and dyadically encrypted data is disclosed. The system preprocesses input data, applies homomorphic-dyadic compression and encryption, tokenizes the compressed data into sourceblocks, and assigns codewords using a codebook. These codewords are processed through a machine learning core, which can be either a conventional transformer-based architecture or a latent transformer core utilizing a variational autoencoder. The system enables secure operations on encrypted data, preserving privacy while allowing complex computations. The processed output is decrypted, decompressed, and translated to match the input modality. A neural upsampler may further enhance the output. The machine learning core is continuously trained using the processed data and additional training data, improving performance over time.
1. A system for deep learning using a large codeword model with homomorphically compressed dyadically encrypted data, comprising:
a computing device comprising at least a memory and a processor;
a plurality of programming instructions stored in the memory and operable on the processor, wherein the plurality of programming instructions, when operating on the processor, cause the computing device to:
receive a plurality of inputs;
preprocess the inputs to generate a plurality of input data sets;
compress and encrypt the input data sets by:
analyzing the input data sets to determine their properties;
creating transformation matrices based on the properties of the input data;
transforming the input data into modified distributions;
generating main data streams of transformed data and secondary data streams of transformation information; and
compressing the main data streams;
tokenize the compressed main data streams into a plurality of sourceblocks;
assign the plurality of sourceblocks a plurality of codewords, where each sourceblock is mapped to a particular codeword through a codebook;
process the plurality of codewords through a machine learning core to generate a codeword response;
translate the codeword response into a translated response which matches the modality of the inputs;
decompress and decrypt the translated response; and
train the machine learning core using the decompressed and decrypted response and a plurality of training data.
2. The system of claim 1 , wherein the machine learning core is a conventional transformer-based architecture comprising:
an embedding layer;
a positional encoding layer; and
a series of transformer layers.
3. The system of claim 2 , further comprising a syntactic splitting component that splits the codewords into smaller units before processing through the conventional transformer-based architecture.
4. The system of claim 1 , wherein the machine learning core is a latent transformer core comprising:
a variational autoencoder with an encoder and a decoder; and
a transformer that processes latent space vectors, wherein the transformer does not include an embedding layer and a positional encoding layer.
5. The system of claim 4 , wherein processing the plurality of codewords through the machine learning core comprises:
generating a plurality of latent space vectors by processing the plurality of codewords through the variational autoencoder's encoder;
learning relationships between the plurality of latent space vectors by processing them through the transformer;
using the learned relationships to generate a plurality of output latent space vectors; and
generating the codeword response by passing the output latent space vectors through the variational autoencoder's decoder.
6. The system of claim 5 , further comprising a syntactic splitting component that splits the latent space vectors into smaller units before processing through the transformer.
7. The system of claim 1 , wherein compressing and encrypting the input data sets further comprises:
combining the compressed main data streams and the secondary data streams into output streams; and
implementing security measures to protect the output streams.
8. The system of claim 7 , wherein the security measures comprise providing cryptographically secure random numbers for use in data transformation and implementing protections against side-channel attacks.
9. The system of claim 1 , wherein transforming the input data into modified distributions comprises transforming the input data into dyadic distributions.
10. The system of claim 1 , further comprising a neural upsampler that processes the codeword response to generate a reconstructed output containing more information than the translated response.
11. A method for deep learning using a large codeword model with homomorphically compressed dyadically encrypted data, comprising the steps of:
receiving a plurality of inputs;
preprocessing the inputs to generate a plurality of input data sets;
compressing and encrypting the input data sets by:
analyzing the input data sets to determine their properties;
creating transformation matrices based on the properties of the input data;
transforming the input data into modified distributions;
generating main data streams of transformed data and secondary data streams of transformation information; and
compressing the main data streams;
tokenizing the compressed main data streams into a plurality of sourceblocks;
assigning the plurality of sourceblocks a plurality of codewords, where each sourceblock is mapped to a particular codeword through a codebook;
processing the plurality of codewords through a machine learning core to generate a codeword response;
translating the codeword response into a translated response which matches the modality of the inputs;
decompressing and decrypting the translated response; and
training the machine learning core using the decompressed and decrypted response and a plurality of training data.
12. The method of claim 11 , wherein the machine learning core is a conventional transformer-based architecture comprising:
an embedding layer;
a positional encoding layer; and
a series of transformer layers.
13. The method of claim 12 , further comprising splitting the codewords into smaller units before processing through the conventional transformer-based architecture.
14. The method of claim 11 , wherein the machine learning core is a latent transformer core comprising:
a variational autoencoder with an encoder and a decoder; and
a transformer that processes latent space vectors, wherein the transformer does not include an embedding layer and a positional encoding layer.
15. The method of claim 14 , wherein processing the plurality of codewords through the machine learning core comprises:
generating a plurality of latent space vectors by processing the plurality of codewords through the variational autoencoder's encoder;
learning relationships between the plurality of latent space vectors by processing them through the transformer;
using the learned relationships to generate a plurality of output latent space vectors; and
generating the codeword response by passing the output latent space vectors through the variational autoencoder's decoder.
16. The method of claim 15 , further comprising splitting the latent space vectors into smaller units before processing through the transformer.
17. The method of claim 11 , wherein compressing and encrypting the input data sets further comprises:
combining the compressed main data streams and the secondary data streams into output streams; and
implementing security measures to protect the output streams.
18. The method of claim 17 , wherein the security measures comprise providing cryptographically secure random numbers for use in data transformation and implementing protections against side-channel attacks.
19. The method of claim 11 , wherein transforming the input data into modified distributions comprises transforming the input data into dyadic distributions.
20. The method of claim 11 , further comprising processing the codeword response through a neural upsampler to generate a reconstructed output containing more information than the translated response.