Abstract: A system and method for efficient data storage, transfer, synchronization, and security using automated model monitoring and training. The system analyzes test datasets to detect data drift, retraining encoding and decoding algorithms as needed. New data sourceblocks are created and assigned codewords, compiling an updated codebook for distribution to connected devices. A novel dyadic distribution subsystem simultaneously compresses and encrypts data by transforming input streams into a dyadic distribution. This process generates a compressed main data stream and a secondary stream of transformation information, which are combined into a secure output. The system includes a network device manager for optimizing codebook distribution based on device resource usage. Operating in both lossless and lossy modes, the system offers flexible, efficient, and secure data handling across various network configurations.

Abstract: A system and methods for integrated data processing and protocol adaptation using dyadic distribution-based compression. The system transforms input data into a dyadic distribution, enabling efficient compression through either variational autoencoders or Huffman encoding. A novel protocol appendix generator creates transformation rules for adapting the compressed data to various network protocols. The system interleaves transformation information with the compressed data, enhancing security and ensuring comprehensive data transmission. An enhanced codeword decoder, employing a hybrid neural network architecture, decodes the data and adapts it to target protocols. The system features a protocol handler using meta-learning techniques for adapting to unfamiliar protocols. Continuous learning mechanisms optimize performance over time.

Abstract: A multi-stage process for homomorphic end-to-end data compression and encryption is claimed. The first stage analyzes input data, applies transformations to increase compressibility and security, produces conditioned data and error streams, transforms data into a dyadic distribution, creates a transformation matrix, performs Huffman coding, and combines Huffman-encoded data with a secondary transformation stream. A second stage further processes the compressed and encrypted output, either repeating the first stage's steps or using an autoencoder for additional compression. The system supports multiple compression stages, each building on the previous one. Decompression reverses these stages while maintaining homomorphic encryption throughout. This approach enables secure data transmission, storage, and analysis in bandwidth-constrained environments, adapting to various data types and compression levels while preserving data quality and security.

Abstract: A system and method for an innovative approach to medical imaging compression and encryption, specifically designed for tomosynthesis data. It transforms input data to a dyadic distribution, optimizing it for Huffman encoding while preserving critical diagnostic information. The compressed data is processed through a Large Codeword Model (LCM) incorporating a latent transformer, which learns complex patterns and relationships within the imaging data. This process not only achieves high compression ratios but also provides inherent encryption. A neural upsampler, trained to invert the dyadic transformation, reconstructs the original image with high fidelity. The system is particularly effective for handling the redundancies inherent in tomosynthesis datasets, where adjacent slices often contain similar information.

Abstract: A multi-stage process for homomorphic end-to-end data compression and encryption is claimed. The first stage analyzes input data, applies transformations to increase compressibility and security, produces conditioned data and error streams, transforms data into a dyadic distribution, creates a transformation matrix, performs Huffman coding, and combines Huffman-encoded data with a secondary transformation stream. A second stage further processes the compressed and encrypted output, either repeating the first stage's steps or using an autoencoder for additional compression. The system supports multiple compression stages, each building on the previous one. Decompression reverses these stages while maintaining homomorphic encryption throughout. This approach enables secure data transmission, storage, and analysis in bandwidth-constrained environments, adapting to various data types and compression levels while preserving data quality and security.

Abstract: This invention is a system and method for encrypted video stream data compaction. The system integrates video processing techniques with data compaction methods, achieving compressed outputs that maintain data security. A video stream processor analyzes frames, estimates motion, and eliminates redundancies, preparing the data for further compression. The processed data is then divided into blocks, mapped to codewords, and subjected to additional compression and encryption rules. This multi-layered approach results in reduced data size and bandwidth requirements for video streaming, while ensuring data integrity and security. The system's adaptability makes it suitable for various video streaming applications, from high-definition content delivery to secure video communications.

Abstract: A system and method for enhancing lossy compressed data. The system receives a compressed data stream, decompresses it, and enhances the decompressed data using adaptive neural network models. Key features include data characteristic analysis, dynamic model selection from multiple specialized neural networks, and quality estimation with feedback-driven optimization. The system adapts to various data types and compression levels, recovering lost information without detailed knowledge of the compression process. It implements online learning for continuous improvement and includes security measures to ensure data integrity. The method is applicable to diverse data types, including financial time-series, images, and audio.

Abstract: A system and method for simultaneous compression and encryption of data. The system analyzes input data to determine its properties and creates a transformation matrix based on these properties. Using this matrix, the input data is transformed into a modified distribution, generating a main data stream of transformed data and a secondary stream of transformation information. The main data stream is compressed, and both streams are combined into a single output. The system implements security measures to protect against various attacks, including side-channel vulnerabilities. By using a dyadic distribution algorithm, the system achieves both compression and encryption in a single pass over the data, offering significant efficiency gains. The system can operate in both lossless and lossy modes, providing flexibility for different application requirements. This approach offers a unique solution for data transmission and storage scenarios where both data reduction and security are critical concerns.

Abstract: A system and method for adaptive data processing combining compression and encryption. The system analyzes input data characteristics, compares probability distributions, and creates a transformation matrix to convert data into a dyadic distribution. It generates a main data stream of transformed data and a secondary stream of transformation information. The system dynamically selects and applies processing techniques, including transformation, encoding, compression, and encryption algorithms, based on analyzed characteristics and real-time performance metrics. It compresses the main data stream using Huffman coding and implements security measures to protect the output. A feedback loop monitors technique effectiveness, updates a knowledge base, and influences future selections. The system can operate in lossless, lossy, or modified lossless modes, adapting to different application requirements.

Abstract: A system and method for data compression with encryption, that produces a conditioned data stream by replacing data blocks within an input data stream to bring the frequency of each data block closer to an ideal value, produces an error stream comprising the differences between the original data and the encrypted data, and compresses the conditioned data.

Abstract: A system and method for efficient data storage, transfer, synchronization, and security using automated model monitoring and training. The system analyzes test datasets to detect data drift, retraining encoding and decoding algorithms as needed. New data sourceblocks are created and assigned codewords, compiling an updated codebook for distribution to connected devices. A novel dyadic distribution subsystem simultaneously compresses and encrypts data by transforming input streams into a dyadic distribution. This process generates a compressed main data stream and a secondary stream of transformation information, which are combined into a secure output. The system includes a network device manager for optimizing codebook distribution based on device resource usage. Operating in both lossless and lossy modes, the system offers flexible, efficient, and secure data handling across various network configurations.

Abstract: A system and methods for integrated data processing and protocol adaptation using dyadic distribution-based compression. The system transforms input data into a dyadic distribution, enabling efficient compression through either variational autoencoders or Huffman encoding. A novel protocol appendix generator creates transformation rules for adapting the compressed data to various network protocols. The system interleaves transformation information with the compressed data, enhancing security and ensuring comprehensive data transmission. An enhanced codeword decoder, employing a hybrid neural network architecture, decodes the data and adapts it to target protocols. The system features a protocol handler using meta-learning techniques for adapting to unfamiliar protocols. Continuous learning mechanisms optimize performance over time.

Abstract: A system and method for efficient data storage, transfer, synchronization, and security using automated model monitoring and training. The system analyzes test datasets to detect data drift, retraining encoding and decoding algorithms as needed. New data sourceblocks are created and assigned codewords, compiling an updated codebook for distribution to connected devices. A novel dyadic distribution subsystem simultaneously compresses and encrypts data by transforming input streams into a dyadic distribution. This process generates a compressed main data stream and a secondary stream of transformation information, which are combined into a secure output. The system includes a network device manager for optimizing codebook distribution based on device resource usage. Operating in both lossless and lossy modes, the system offers flexible, efficient, and secure data handling across various network configurations.

Abstract: A system and method for intrusion detection with prediction and validation subsystems powered by machine learning. The system analyzes real-time codeword streams and historical data to predict potential intrusions before they fully manifest. It employs various machine learning models to extract features, identify patterns, and validate detected anomalies. The system continuously learns from validated events and false positives, improving its accuracy over time. An integrated encryption module secures sensitive data using a dyadic distribution-based algorithm, combining compression and encryption. This approach significantly reduces false positives, enhances threat detection capabilities, and provides robust data protection for cybersecurity applications.

Abstract: A system and method for implementing multilayer security in documents using multiple codebooks. The system encodes different sections of a document using codebooks associated with specific security clearance levels. Users can only access sections of the document for which they have appropriate clearance, with unauthorized sections appearing redacted. The system provides granular, section-level security while also offering data compression benefits. Additional features include dynamic reclassification, hierarchical codebooks, expiring access, multi-factor authentication, and user-specific watermarking.

Abstract: A system and method for data compression with encryption, that produces a conditioned data stream by replacing data blocks within an input data stream to bring the frequency of each data block closer to an ideal value, produces an error stream comprising the differences between the original data and the encrypted data, and compresses the conditioned data.

Abstract: A system and method for simultaneous compression and encryption of data. The system analyzes input data to determine its properties and creates a transformation matrix based on these properties. Using this matrix, the input data is transformed into a modified distribution, generating a main data stream of transformed data and a secondary stream of transformation information. The main data stream is compressed, and both streams are combined into a single output. The system implements security measures to protect against various attacks, including side-channel vulnerabilities. By using a dyadic distribution algorithm, the system achieves both compression and encryption in a single pass over the data, offering significant efficiency gains. The system can operate in both lossless and lossy modes, providing flexibility for different application requirements. This approach offers a unique solution for data transmission and storage scenarios where both data reduction and security are critical concerns.

Abstract: A system and method for data compression with encryption, that produces a conditioned data stream by replacing data blocks within an input data stream to bring the frequency of each data block closer to an ideal value, produces an error stream comprising the differences between the original data and the encrypted data, and compresses the conditioned data.

Abstract: A system and method for data compression with encryption, that produces a conditioned data stream by replacing data blocks within an input data stream to bring the frequency of each data block closer to an ideal value, produces an error stream comprising the differences between the original data and the encrypted data, and compresses the conditioned data.

Abstract: A system and method for data compression with encryption, that produces a conditioned data stream by replacing data blocks within an input data stream to bring the frequency of each data block closer to an ideal value, produces an error stream comprising the differences between the original data and the encrypted data, and compresses the conditioned data.