Unified platform for multi-type data compression and decompression using homomorphic encryption and neural upsampling
A unified platform for multi-type data compression and decompression is disclosed. The platform employs a virtual management layer to receive, organize, and route input data to corresponding compression subsystems. Multiple compression methods, including homomorphic encryption-based techniques, are utilized to compress data sets while maintaining data privacy. A data manager associates and manages related data sets throughout the compression and decompression processes. Compressed data is routed to appropriate decompression subsystems, where it is decompressed and reconstructed using advanced techniques, such as neural upsampling, to recover lost information and enhance data quality. The platform supports various data types and compression methods, enabling efficient and secure compression and decompression of data. By integrating homomorphic encryption and data reconstruction techniques, the platform provides a comprehensive solution for data compression and decompression while preserving data privacy and enhancing data quality.
1. A unified platform for multi-type data compression and decompression, comprising:
a computing device comprising at least a memory and a processor;
a plurality of programming instructions that, when operating on the processor, cause the computing device to:
receive input data;
organize the input data into a plurality of data sets;
route each data set to a corresponding compression subsystem;
compress each data set using its corresponding compression subsystem;
perform operations on the compressed data sets while maintaining data privacy;
receive compressed data at a decompression pipeline;
route the compressed data to corresponding decompression subsystems based on compression techniques used;
decompress compressed data using appropriate decompression subsystems;
apply data reconstruction techniques to the decompressed data to recover lost information and enhance data quality; and
output final reconstructed data.
2. The platform of claim 1 , wherein a first compression subsystem uses variational autoencoder-based homomorphic compression.
3. The platform of claim 2 , wherein the first compression subsystem:
compresses data sets into lower-dimensional latent space representations using a variational autoencoder; and
encrypts the latent space representations using a homomorphic encryption scheme.
4. The platform of claim 1 , wherein a second compression subsystem uses codebook-based homomorphic compression method.
5. The platform of claim 4 , wherein the second compression subsystem:
quantizes data sets into finite sets of intervals represented by unique codewords;
generates codebooks by assigning codewords to intervals based on selected codebook generation techniques; and
compresses quantized data sets by replacing each interval with its corresponding codeword from the generated codebooks.
6. The platform of claim 1 , further comprising a data manager comprising a second plurality of programming instructions that, when operating on the processor, cause the computing device to:
flag and associate related data sets; and
track and manage associations between data sets throughout compression and decompression processes.
7. The platform of claim 1 , wherein applying data reconstruction techniques comprises utilizing a neural upsampling subsystem comprising a third plurality of programming instructions that, when operating on the processor, cause the computing device to:
receive decompressed data from the decompression subsystems;
apply trained neural networks to the decompressed data to recover lost information and enhance decompressed data quality; and
output upsampled data as final reconstructed outputs.
8. The platform of claim 7 , wherein the neural upsampling subsystem is trained on a diverse dataset that includes compressed and original data pairs.
9. The platform of claim 7 , wherein the neural upsampling subsystem supports various neural network architectures, including autoencoders, convolutional neural networks, and recurrent neural networks.
10. A method for multi-type data compression and decompression, comprising the steps of:
receiving input data;
organizing the input data into a plurality of data sets;
routing each data set to a corresponding compression subsystem;
compressing each data set using its corresponding compression subsystem;
performing operations on the compressed data sets while maintaining data privacy;
receiving compressed data at a decompression pipeline;
routing the compressed data to corresponding decompression subsystems based on compression techniques used;
decompressing compressed data using appropriate decompression subsystems;
applying data reconstruction techniques to the decompressed data to recover lost information and enhance data quality; and
outputting final reconstructed data.
11. The method of claim 10 , wherein a first compression subsystem uses variational autoencoder-based homomorphic compression.
12. The method of claim 11 , wherein the variational autoencoder-based homomorphic compression method further comprises the steps of:
compressing data sets into lower-dimensional latent space representations using a variational autoencoder; and
encrypting the latent space representations using a homomorphic encryption scheme.
13. The method of claim 10 , wherein a second compression subsystem uses codebook-based homomorphic compression method.
14. The method of claim 13 , wherein the second compression subsystem:
quantizes data sets into finite sets of intervals represented by unique codewords;
generates codebooks by assigning codewords to intervals based on selected codebook generation techniques; and
compresses quantized data sets by replacing each interval with its corresponding codeword from the generated codebooks.
15. The method of claim 10 , wherein performing operations on the compressed data sets comprises performing homomorphic operations on encrypted data.
16. The method of claim 10 , further the step of:
flagging and associating related data sets; and
tracking and managing associations between data sets throughout compression and decompression processes.
17. The method of claim 10 , wherein applying data reconstruction techniques comprises utilizing a neural upsampling subsystem to perform the steps of:
receiving decompressed data from the decompression subsystems;
applying trained neural networks to the decompressed data to recover lost information and enhance decompressed data quality; and
outputting upsampled data as final reconstructed outputs.
18. The method of claim 17 , wherein the neural upsampling subsystem is trained on a diverse dataset that includes compressed and original data pairs.
19. The method of claim 17 , wherein the neural upsampling subsystem supports various neural network architectures, including autoencoders, convolutional neural networks, and recurrent neural networks.