Abstract: A system and method for codebook-based data encoding. Portions of the data are encoded by different encoding libraries, depending on which library provides the greatest compaction for a given portion of the data. This methodology not only provides substantial improvements in data compaction over use of a single data compaction algorithm with the highest average compaction, but provides substantial additional security in that multiple decoding libraries must be used to decode the data. In some embodiments, each portion of data may further be encoded using different sourceblock sizes, providing further security enhancements as decoding requires multiple decoding libraries and knowledge of the sourceblock size used for each portion of the data. In some embodiments, encoding libraries may be randomly or pseudo-randomly rotated to provide additional security.

Abstract: A system and method for distributed node-based data compaction. The system uses machine learning on data chunks to generate codebooks which compact the data to be stored, processed, or sent with a smaller data profile than uncompacted data. The system uses a data compaction in an existing blockchain fork or implemented in a new blockchain protocol from which nodes that wish to or need to use the blockchain can do so with a reduced storage requirement. The system uses network data compaction across all nodes to increase the speed of and decrease the size of a blockchain's data packets. The system uses data compaction firmware to increase the efficiency at which mining rigs can computationally validate new blocks on the blockchain. The system can be implemented using any combination of the three data compaction services to meet the needs of the desired blockchain technology.

Abstract: A system and method for manipulation of compacted data files, utilizing a reference codebook, a random-access engine, a data deconstruction engine, and a data deconstruction engine. The system may receive a data query pertaining to a data read or data write request, wherein the data file to be read from or written to is a compacted data file. A random-access engine may facilitate data manipulation processes by accessing a reference codebook associated with the compacted data file, a frequency table used to construct the reference codebook, and data query details. A data read request is supported by random-access search capabilities that may enable the locating and decoding of the bits corresponding to data query details. A random-access engine facilitates data write processes. The random-access engine may encode the data to be written, insert the encoded data into a compacted data file, and update the codebook as needed.

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 compacting data that uses mismatch probability estimation to improve entropy encoding methods to account for, and efficiently handle, previously-unseen data in data to be compacted. Training data sets are analyzed to determine the frequency of occurrence of each sourceblock in the training data sets. A mismatch probability estimate is calculated comprising an estimated frequency at which any given data sourceblock received during encoding will not have a codeword in the codebook. Entropy encoding is used to generate codebooks comprising codewords for data sourceblocks based on the frequency of occurrence of each sourceblock. A “mismatch codeword” is inserted into the codebook based on the mismatch probability estimate to represent those cases when a block of data to be encoded does not have a codeword in the codebook. During encoding, if a mismatch occurs, a secondary encoding process is used to encode the mismatched sourceblock.

Abstract: A system and method for data compaction and encryption of anonymized data records. A dataset may be pre-processed by dividing into a plurality of sourceblocks at all reasonable sourceblock lengths, and then counting how many times each sourceblock occurs in the dataset, resulting in a tally record of tokens and their count value. This tally record may then be anonymized and transmitted to a data deconstruction engine which combined with a library manager creates a codebook and performs optimization techniques on the codebook. The received anonymized tally record may be parsed into individual tokens by identifying the tokens with the highest count value. The tokens may then be sent, in descending order of count value, to the library manger where each token may be assigned a codeword. A half-backed codebook is then created using the tokens and each token's unique codeword, before sending the half-backed codebook to a system user.

Abstract: A system and method for encrypted data compression, which uses frequency analysis on data blocks within an input data stream to produce a prefix table, representing a first layer of transformation, and which applies a Burrow's-Wheeler transform (BWT) to the data inside the prefix table, representing a second layer of transformation, and which compresses the transformed data. In some implementations, the system and method may further include applying the BWT to a conditioned stream of genomic data, wherein the conditioned stream of data is accompanied by an error stream comprising the differences between the original data and the encrypted data.

Abstract: A system and method for random-access manipulation of compacted data files, utilizing a reference codebook, a random-access engine, a data deconstruction engine, and a data deconstruction engine. The system may receive a data query pertaining to a data read or data write request, wherein the data file to be read from or written to is a compacted data file. A random-access engine may facilitate data manipulation processes by accessing a reference codebook associated with the compacted data file, a frequency table used to construct the reference codebook, and data query details. A data read request is supported by random-access search capabilities that may enable the locating and decoding of the bits corresponding to data query details. A random-access engine facilitates data write processes. The random-access engine may encode the data to be written, insert the encoded data into a compacted data file, and update the codebook as needed.

Abstract: A system and method for securing high-speed communications between processing units on computer chips, wherein a training data set is used to find patterns and associated smaller indices, or codewords, which are stored in a reference codebook library, and where reconstruction and deconstruction algorithms are used to encode and decode data as it is received. The codebook and algorithms may be stored in the firmware of a semiconductor which enable reduced resources and cost when transmitting data between or among devices that utilize such semiconductors.

Abstract: A system and method for compaction of floating-point numbers within a dataset, comprising a pre-encoder, a data deconstruction engine, a library manager, a codeword storage, and a data reconstruction engine. A pre-encoder may receive a plurality of data sourcepackets with may contain one or more floating-point numbers and the received data sourcepackets are scanned to identify floating-point numbers and the identified floating-point numbers. Identified floating-point numbers may be pre-encoded into binary string representations which are low-distortion embeddings of real numbers into a Hamming space. The binary string representation may be indexed to indicate it represents a floating-point number before being compacted by a data deconstruction engine and library manager. The pre-encoding of floating-point numbers located within a sourcepacket enables the system to maximize the benefit of the compaction capabilities of the data deconstruction engine.

Abstract: A system and method for low-distortion compaction of floating-point numbers comprising a pre-encoder, a data deconstruction engine, a library manager, a codeword storage, and a data reconstruction engine. A pre-encoder may receive a plurality of data sourcepackets with may contain one or more floating-point numbers and the received data sourcepackets are scanned to identify floating-point numbers and the identified floating-point numbers. Identified floating-point numbers may be pre-encoded into binary string representations which are low-distortion embeddings of real numbers into a Hamming space. The binary string representation may be indexed to indicate it represents a floating-point number before being compacted by a data deconstruction engine and library manager. The pre-encoding of floating-point numbers located within a sourcepacket enables the system to maximize the benefit of the compaction capabilities of the data deconstruction engine.

Abstract: A system and method for encoding data using a plurality of encoding libraries. Portions of the data are encoded by different encoding libraries, depending on which library provides the greatest compaction for a given portion of the data. This methodology not only provides substantial improvements in data compaction over use of a single data compaction algorithm with the highest average compaction, but provides substantial additional security in that multiple decoding libraries must be used to decode the data. In some embodiments, each portion of data may further be encoded using different sourceblock sizes, providing further security enhancements as decoding requires multiple decoding libraries and knowledge of the sourceblock size used for each portion of the data. In some embodiments, encoding libraries may be randomly or pseudo-randomly rotated to provide additional security.

Abstract: A system and method for file type identification involving extraction of a file-print of a file, the file-print being a unique or practically-unique representation of statistical characteristics associated with the distribution of bits in the binary contents of the file, similar to a fingerprint. The file-print is then passed to a machine learning algorithm that has been trained to recognize file types from their file-prints. The machine learning algorithm returns a predicted file type and, in some cases, a probability of correctness of the prediction. The file may then be encoded using an encoding algorithm chosen based on the predicted file type.

Abstract: A system and method for error-resilient data reduction, utilizing a phase detector, a data requestor, a multi-phase trainer, a reconstruction engine, a deconstruction engine, and one or more reference codebooks. A multi-phase trainer may be used to train the reconstruction and deconstruction engines on various phase sourceblocks in order recover quickly from corrupted data files that cause the phase alignment of the sourceblocks to become out of phase. A phase detector may determine when the sourceblocks get out of phase and when the return to in-phase by checking if a predetermined threshold probability of correct encoding is met. Data requestor may request for retransmission only the data that was received out of phase.

Abstract: The inventor has conceived, and reduced to practice, a system and method for data compaction using that applies delta encoding methods to entropy encoding methods to improve data compaction of entropy encoding methods under certain conditions and when compacting data having certain characteristics. Delta encoding may be applied to entropy encoding methods to further compact data sets by reducing the number of sourceblocks included in a codebook to those most commonly encountered in data to be encoded and, where mismatches occur during encoding, using delta encoding of bit differences with existing sourceblocks in the codebook rather than adding new sourceblocks to the codebook.

Abstract: A system and method for faster communication between blockchain mining nodes and faster block validation. The system uses machine learning on data chunks to generate codebooks which compact the data to be stored, processed, or sent with a smaller data profile than uncompacted data. The system uses a data compaction in an existing blockchain fork or implemented in a new blockchain protocol from which nodes that wish to or need to use the blockchain can do so with a reduced storage requirement. The system uses network data compaction across all nodes to increase the speed of and decrease the size of a blockchain's data packets. The system uses data compaction firmware to increase the efficiency at which mining rigs can computationally validate new blocks on the blockchain. The system can be implemented using any combination of the three data compaction services to meet the needs of the desired blockchain technology.

Abstract: A system and method for compacting data that uses mismatch probability estimation to improve entropy encoding methods to account for, and efficiently handle, previously-unseen data in data to be compacted. Training data sets are analyzed to determine the frequency of occurrence of each sourceblock in the training data sets. A mismatch probability estimate is calculated comprising an estimated frequency at which any given data sourceblock received during encoding will not have a codeword in the codebook. Entropy encoding is used to generate codebooks comprising codewords for data sourceblocks based on the frequency of occurrence of each sourceblock. A “mismatch codeword” is inserted into the codebook based on the mismatch probability estimate to represent those cases when a block of data to be encoded does not have a codeword in the codebook. During encoding, if a mismatch occurs, a secondary encoding process is used to encode the mismatched sourceblock.

Abstract: A system and method for file type identification involving extraction of a file-print of a file, the file-print being a unique or practically-unique representation of statistical characteristics associated with the distribution of bits in the binary contents of the file, similar to a fingerprint. The file-print is then passed to a machine learning algorithm that has been trained to recognize file types from their file-prints. The machine learning algorithm returns a predicted file type and, in some cases, a probability of correctness of the prediction. The file may then be encoded using an encoding algorithm chosen based on the predicted file type.

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 genomic encryption, which uses frequency analysis on data blocks within an input data stream to produce a prefix table, representing a first layer of transformation, and which applies a Burrow's-Wheeler transform (BWT) to the data inside the prefix table, representing a second layer of transformation, and which compresses the transformed data. In some implementations, the system and method may further include applying the BWT to a conditioned stream of genomic data, wherein the conditioned stream of genomic data is accompanied by an error stream comprising the differences between the original data and the encrypted data.