Abstract: A system and method for data compression with protocol adaptation, that utilizes a codebook generator which leverages one or more machine/deep learning algorithms trained on at least a plurality of protocol policies in order to generate a protocol appendix and codebook, wherein original data is encoded by an encoder according to the codebook and sent to a decoder, but instead of just decoding the data according to the codebook to reconstruct the original data, data manipulation rules such as mapping and transformation are applied at the decoding stage to transform the decoded data into protocol formatted data.

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 scriptable selective obfuscation of records comprising a data obfuscation module configured to identify and perform data anonymization on personal identifiable information (PII) contained within a plurality of records to create a partially-blurred dataset, and further comprising an encoder which receives the partially-blurred dataset and performs data compaction on the partially-blurred dataset before storing the compacted dataset in a data storage system. In some implementations, the data storage system is a blockchain database and the system functions as a clearinghouse to validate and monitor transactions involving data access rights between record owners and third-party entities. The system can further broker such transactions and direct payment form the third-party entity to the record owner when access rights have been purchased.

Abstract: A system and methods for data compression with encryption using asymmetric codebooks, that utilizes machine learning to generate asymmetric encoder and decoder codebooks for data encoding and compression. The use of asymmetric codebooks enables a variety of data obfuscation techniques such that output data remains useful to the decoder and any downstream entity, while concealing the original sourceblock data. The asymmetric codebooks may be utilized in reverse order to restore the original data as needed, facilitating a two-way reversible operation.

Abstract: A system and method for data compaction utilizing distributed codebook encoding to improve entropy encoding methods to account for, and efficiently handle, previously-unseen data in data to be compacted, allow for distributed encoding and decoding capabilities, and allow for parametrized codebook encoding methods. 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. Further, a codebook and a behavior codebook may both be maintained or altered in a distributed fashion across multiple devices or services, for widespread, or permission-based, or parametrized codebook encoding.

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 data compression with protocol adaptation, that utilizes a codebook generator which leverages one or more machine/deep learning algorithms trained on at least a plurality of protocol policies in order to generate a protocol appendix and codebook, wherein original data is encoded by an encoder according to the codebook and sent to a decoder, but instead of just decoding the data according to the codebook to reconstruct the original data, data manipulation rules such as mapping and transformation are applied at the decoding stage to transform the decoded data into protocol formatted data.

Abstract: Codebook data compaction using a universal codebook and mismatch probability estimations to improve entropy encoding methods. 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.

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 filesystem data compression using codebooks, that measures in real-time the probability distribution of an encoded data stream, compares the probability distribution to a reference probability distribution, and uses one or more statistical algorithms to determine the divergence between the two sets of probability distributions to determine if an unusual distribution is the result of a data intrusion. The system comprises both encoding and decoding machines, an intrusion detection module, a codebook training module, and various databases which perform various analyses on encoded data streams. Further, the system comprises a system for integrating the compression into a filesystem for both system-wide compression on a per-file or filegroup basis, and intrusion or alteration detection of files.

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.