Abstract: An apparatus for physically encrypting nucleic acid, wherein the apparatus include at least a computing device configured to receive a nucleic acid sample from a subject, input a secret defined by the subject, generate a nucleic acid codebook as a function of the secret, wherein the nucleic acid codebook include a code pattern and a plurality of nucleic acid keys correlates to a plurality of nucleic acid values, and at least a manufacturing device configured to encrypt a nucleic acid sample received from a subject as a function of the nucleic acid codebook, and output an encrypted nucleic acid sample.

Abstract: An apparatus for secure multiparty computations for machine-learning is presented. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory contains instructions configuring the at least a processor to submit a secure multiparty computation request onto an immutable sequential listing, wherein the secure multiparty computation request includes a contingent payment and an authenticity commitment of a first private dataset, receive at least a participant commitment from each participating device of a quorum of participating devices, generate a first localized model as a function of the first private dataset, and perform a joint training protocol as a function of the first localized model and a second localized model from the quorum of participating devices, wherein the joint training protocol includes generating a joint training datum.

Abstract: A method of cryptographically secured decentralized testing includes receiving, by a computing device and from a secure test apparatus, an output of a cryptographic function of a secret test result identifier, authenticating the output, and recording, in a data repository, an indication of a test result as a function of the output.

Abstract: A method of cryptographically secured decentralized testing includes receiving, by a computing device and from a secure test apparatus, an output of a cryptographic function of a secret test result identifier, authenticating the output, and recording, in a data repository, an indication of a test result as a function of the output.

Abstract: A method of cryptographically secured decentralized testing includes receiving, by a computing device and from a secure test apparatus, an output of a cryptographic function of a secret test result identifier, authenticating the output, and recording, in a data repository, an indication of a test result as a function of the output.

Abstract: A system and method provide a sequence learning model. The method for training the sequence learning model comprises retrieving input sequence data. The input sequence data includes one or more input time sequences. The method also encodes the input sequence data into output symbol data using a sequence learning model. The output symbol data includes one or more symbolic representations. The method decodes, based on a neural network, the output symbol data to decoded sequence data, where the decoded sequence data includes one or more decoded time sequences that are to match the one or more input time sequences in the input sequence data. The method further compares the decoded sequence data with the input sequence data and updates the sequence learning model based on the comparison.

Abstract: A system for trustlessly proving possession of secured data includes a storing device designed and configured to store a collection of data, divide the collection of data into a plurality of lots, receive a cryptographic accumulator of the collection of data, the cryptographic accumulator having a root and a plurality of accumulated elements, wherein each accumulated element is produced by performing an encryption process on each lot of the plurality of lots, evaluate a posting of the root to an immutable sequential listing, select an accumulated element as a function of the root and a public pseudorandom function, generate a secure proof of possession of a data lot used to produce the accumulated element and transmit the secure proof and the accumulated element.

Abstract: A system and method provide a sequence learning model. The method for training the sequence learning model comprises retrieving input sequence data. The input sequence data includes one or more input time sequences. The method also encodes the input sequence data into output symbol data using a sequence learning model. The output symbol data includes one or more symbolic representations. The method decodes, based on a neural network, the output symbol data to decoded sequence data, where the decoded sequence data includes one or more decoded time sequences that are to match the one or more input time sequences in the input sequence data. The method further compares the decoded sequence data with the input sequence data and updates the sequence learning model based on the comparison.

Abstract: A client device retrieves a diarization model. The diarization model has been trained to determine whether there is a change of one speaker to another speaker within an audio sequence. The client device receives enrollment data from each speaker of a group of speakers who are participating in an audio conference. The client device obtains an audio segment from a recording of the audio conference. The client device identifies one or more speakers for the audio segment by applying the diarization model to a combination of the enrollment data and the audio segment.