Abstract: A method, apparatus, and computer program product for offline authentication are provided. An example method includes receiving, by a computing device, a request for authentication from a first user device associated with a first user. The request includes first authentication credentials generated based upon user attributes retrieved by the first user device from a digital identity construct database at a first time. The method includes determining an offline condition of the computing device at a first time. The method also includes obtaining, by the computing device, second authentication credentials associated with the first user that are based upon one or more user attributes retrieved by the computing device from the digital identity construct database at a second time later than the first time. The method incudes determining a discrepancy between the first and second authentication credentials and authenticating the first user based upon a forecast operation of the same.

Abstract: A method, apparatus, and computer program product for proactive offline authentication are provided. An example method includes determining a current offline condition of a computing device at a first time and determining a prior online condition of the computing device at a second time that is earlier than the first time at which the computing device generated second authentication credentials based upon one or more user attributes obtained from a digital identity construct database associated with a first user at the second time. The method further includes obtaining, at the first time, first authentication credentials associated with the first user and determining a discrepancy between the first and the second authentication credentials. In response to the determined discrepancy, the method includes generating an authentication token based upon the second authentication credentials for authenticating a first user device of the first user with the computing device.

Abstract: Apparatuses, methods, and computer program products are provided for improved model compliance. An example method includes receiving a product model that is generated from user data associated with a plurality of users and receiving a first regulation offending model that is non-compliant with respect to a first regulatory factor. The method also includes analyzing the product model with the first regulation offending model and generating a first regulation compliance score for the product model with respect to the first regulatory factor. The method further includes determining whether the first regulation compliance score satisfies a first regulatory factor threshold. In an instance in which the first regulation compliance score fails to satisfy the first regulatory factor threshold, the method includes generating a first violation notification or modify the product model.

Abstract: Systems, methods, and computer program products are provided for disparate quantum computing (QC) detection. An example system includes QC detection data generation circuitry that generates a first set of QC detection data and generates a second set of QC detection data. The system also includes cryptographic circuitry that generates a first public cryptographic key and a first private cryptographic key via a first post-quantum cryptographic (PQC) technique and generates a second public cryptographic key and a second private cryptographic key via a second PQC technique. The cryptographic circuitry further generates encrypted first QC detection, second QC detection data, and destroys the first private cryptographic key and the second private cryptographic key. The system further includes data monitoring circuitry that monitors for the first encrypted QC detection data and the second encrypted QC detection data.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for gathering performance information for post-quantum cryptography (PQC) is provided. An example method includes generating, by a QC detection data generation circuitry, QC detection data. The example method further includes encrypting, by a PQC cryptographic circuitry, the QC detection data based on a PQC cryptographic technique The example method further includes decrypting, by a PQC decryption circuitry, the QC detection data. The example method further includes storing, by a PQC cryptographic performance circuitry, encryption metadata generated by the PQC cryptographic circuitry and decryption metadata generated by the PQC decryption circuitry as PQC cryptographic performance information associated with the PQC cryptographic technique.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for post-quantum cryptography (PQC). An example method includes receiving data, a set of data attributes about the data, and a risk profile data structure indicative of a vulnerability of the data in a PQC data environment. The example method further includes retrieving PQC cryptographic performance information associated with a set of PQC cryptographic techniques. The PQC cryptographic performance information may comprise a set of PQC cryptographic performance attributes for a plurality of PQC cryptographic techniques in the set of PQC cryptographic techniques. The example method further includes selecting a PQC encryption algorithm for encrypting the data based on the set of data attributes, the risk profile data structure, the PQC cryptographic performance information, and a PQC optimization machine learning model. Subsequently, the example method includes encrypting the data based on the selected PQC encryption algorithm.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for gathering performance information for post-quantum cryptography (PQC) is provided. An example method includes generating, by a quantum computing (QC) detection data generation circuitry, QC detection data and encrypting, by a PQC cryptographic circuitry and by a first neural network, the QC detection data based on a PQC technique, where the first neural network is trained to encrypt the QC detection data using the PQC technique. The example method further includes decrypting, by a PQC decryption circuitry and by a second neural network, the encrypted QC detection data wherein the second neural network is trained to decrypt data, and storing encryption metadata and decryption metadata as PQC cryptographic performance information associated with the PQC technique.

Abstract: Various systems and methods are provided for quantum computing based optimization of an efficient frontier determination. One exemplary method may comprise identifying one or more filtered portfolio optimization factor data based on one or more portfolio optimization factor data, QC algorithms, and algorithm performance information, selecting one QC algorithm for each filtered portfolio optimization factor data of the one or more filtered portfolio optimization factor data, and utilizing the selected QC algorithm to optimize an efficient frontier determination for each identified filtered portfolio optimization factor data.

Abstract: Apparatuses, methods, and computer program products are provided for improved model compliance. An example method includes receiving a product model that is generated from user data associated with a plurality of users and receiving a first regulation offending model that is non-compliant with respect to a first regulatory factor. The method also includes analyzing the product model with the first regulation offending model and generating a first regulation compliance score for the product model with respect to the first regulatory factor. The method further includes determining whether the first regulation compliance score satisfies a first regulatory factor threshold. In an instance in which the first regulation compliance score fails to satisfy the first regulatory factor threshold, the method includes generating a first violation notification or modify the product model.

Abstract: Various systems and methods are provided for quantum computing based optimization of a personalized portfolio. One exemplary method may comprise identifying one or more filtered personalized portfolio optimization factor data based on one or more optimization factor data for the personalized portfolio, personalized portfolio owner feedback, QC algorithms, and algorithm performance information, selecting one QC algorithm for each filtered portfolio optimization factor data of the one or more filtered portfolio optimization factor data, utilizing the selected QC algorithm to optimize a personalized portfolio determination for each identified filtered personalized portfolio optimization factor data, and rebalancing the personalized portfolio based on the personalized portfolio determination.

Abstract: Apparatuses, methods, and computer program products are provided for data privacy and clearance traversal. An example method includes receiving a first model having an associated first clearance. The method further includes determining a faulty outcome of the first model due to the first clearance and receiving a second model having an associated second clearance. The method also includes inputting the faulty outcome of the first model to the second model and generating a traversal outcome of the second model. The method further include inputting the traversal outcome to the first model so as to traverse a clearance issue associated with the first clearance. The method also includes preventing access of the second model to user data analyzed by the first model in generating the faulty outcome.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for post-quantum cryptography (PQC). An example system includes a PQC smartcard. The smartcard may include a PQC cryptographic algorithm selection circuitry configured to select a PQC cryptographic technique from a set of PQC cryptographic techniques for encrypting the data. The smartcard may further include a PQC cryptographic circuitry configured to encrypt data based on a generated set of PQC encryption attributes and the PQC cryptographic technique.

Abstract: Systems, methods and computer program products are provided for layered quantum computing (QC) detection. An example system includes QC detection data generation circuitry that generates QC detection data via a first post-quantum cryptographic (PQC) technique. The system also includes cryptographic circuitry configured to generate a pair of asymmetric cryptographic keys including a public cryptographic key and a private cryptographic key via a second PQC technique, generate encrypted QC detection data based on the pair of asymmetric cryptographic keys, and destroy the private cryptographic key. The system further includes data monitoring circuitry configured to monitor a set of data environments for electronic information related to the encrypted QC detection data. In response to detection of the electronic information related to the encrypted QC detection data, the system may monitor a set of data environments for electronic information related to the QC detection data.

Abstract: Systems, methods, and computer program products are provided for disparate quantum computing (QC) detection. An example system includes QC detection data generation circuitry that generates a first set of QC detection data and generates a second set of QC detection data. The system also includes cryptographic circuitry that generates a first public cryptographic key and a first private cryptographic key via a first post-quantum cryptographic (PQC) technique and generates a second public cryptographic key and a second private cryptographic key via a second PQC technique. The cryptographic circuitry further generates encrypted first QC detection, second QC detection data, and destroys the first private cryptographic key and the second private cryptographic key. The system further includes data monitoring circuitry that monitors for the first encrypted QC detection data and the second encrypted QC detection data.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for post-quantum cryptography (PQC). An example method includes receiving data, a set of data attributes about the data, and a risk profile data structure indicative of a vulnerability of the data in a PQC data environment. The example method further includes retrieving PQC cryptographic performance information associated with a set of PQC cryptographic techniques. The PQC cryptographic performance information may comprise a set of PQC cryptographic performance attributes for each PQC cryptographic technique in the set of PQC cryptographic techniques. The example method further includes selecting a PQC encryption algorithm for encrypting the data based on the set of data attributes, the risk profile data structure, the PQC cryptographic performance information, and a PQC optimization machine learning model. Subsequently, the example method includes encrypting the data based on the selected PQC encryption algorithm.

Abstract: Various systems and methods are provided for quantum computing based optimization of an efficient frontier determination. One exemplary method may comprise identifying one or more filtered portfolio optimization factor data based on one or more portfolio optimization factor data, QC algorithms, and algorithm performance information, selecting one QC algorithm for each filtered portfolio optimization factor data of the one or more filtered portfolio optimization factor data, and utilizing the selected QC algorithm to optimize an efficient frontier determination for each identified filtered portfolio optimization factor data.

Abstract: Various systems and methods are provided for quantum computing based optimization of stress testing. One exemplary method may comprise receiving a stress testing scenario, identifying one or more filtered stress testing factor data based on one or more stress testing factor data, stress testing scenario data, QC algorithms, and algorithm performance information, selecting one QC algorithm for each filtered stress testing factor data of the one or more filtered stress testing factor data, and utilizing the selected QC algorithm for each filtered stress testing factor data to optimize a stress testing determination for each identified filtered stress test factor data.

Abstract: Various systems and methods are provided for quantum computing based optimization of a personalized portfolio. One exemplary method may comprise identifying one or more filtered personalized portfolio optimization factor data based on one or more optimization factor data for the personalized portfolio, personalized portfolio owner feedback, QC algorithms, and algorithm performance information, selecting one QC algorithm for each filtered portfolio optimization factor data of the one or more filtered portfolio optimization factor data, utilizing the selected QC algorithm to optimize a personalized portfolio determination for each identified filtered personalized portfolio optimization factor data, and rebalancing the personalized portfolio based on the personalized portfolio determination.

Abstract: Apparatuses, methods, and computer program products are provided for improved model compliance. An example method includes receiving a product model that is generated from user data associated with a plurality of users and receiving a first regulation offending model that is non-compliant with respect to a first regulatory factor. The method also includes analyzing the product model with the first regulation offending model and generating a first regulation compliance score for the product model with respect to the first regulatory factor. The method further includes determining whether the first regulation compliance score satisfies a first regulatory factor threshold. In an instance in which the first regulation compliance score fails to satisfy the first regulatory factor threshold, the method includes generating a first violation notification or modify the product model.

Abstract: Apparatuses, methods, and computer program products are provided for improved data privacy. An example method includes receiving a standard model where the standard model includes user data associated with a plurality of users, and the user data is associated with one or more privacy factors. The method also includes receiving a first privacy impact model that identifies a first privacy factor and analyzing the standard model with the first privacy impact model. The method also includes generating a first privacy impact score for the first privacy factor. The method may further include determining if the first privacy impact score satisfies a first privacy factor threshold. In an instance in which the first privacy impact score fails to satisfy the first privacy factor threshold, the method may generate a first violation notification or augment the standard model.