System and method for generating decoy data and sharding sensitive data utilizing quantum computing
A system includes a memory configured to store instances of a software application executable on a computing device and a set of sensitive data and a processor operably coupled to the memory and configured to detect an interaction to initiate an execution of user interactions with the set of sensitive data. The processor is further configured to execute one or more generative machine-learning models trained to generatively present sequences of different decoy data to a user in response to an execution of interactions with the different decoy data, partition the set of sensitive data into sets of sharded sensitive data, and transfer the sets of sharded sensitive data to a trusted database and onto a trusted computing node. The processor is further configured to store a log of the generatively presented sequences of different decoy data and the execution of the interactions with the different decoy data.
1 . A system, comprising:
a memory configured to store one or more instances of a software application executable on a computing device and a set of sensitive data associated with at least one instance of the software application; and
one or more processors operably coupled to the memory and configured to:
detect an interaction to initiate an execution of one or more user interactions with the set of sensitive data associated with the at least one instance of the software application, and, in response:
execute one or more generative machine-learning models trained to generatively present sequences of different decoy data to a user in response to an execution of one or more user interactions with the different decoy data; and
while generatively presenting the sequences of different decoy data to the user:
partition the set of sensitive data into one or more sets of sharded sensitive data to be transferred to a trusted database of a predetermined plurality of trusted databases and onto a trusted computing node of a predetermined plurality of trusted computing nodes;
transfer the one or more sets of sharded sensitive data to the trusted database and onto the trusted computing node, wherein the one or more sets of sharded sensitive data is transferred to the trusted database and onto the trusted computing node for obfuscating from view the set of sensitive data; and
in response to determining at least a partial completion of the execution of the one or more user interactions with the different decoy data, store a log of the generatively presented sequences of different decoy data and the execution of the one or more user interactions with the different decoy data.
2 . The system of claim 1 , wherein the one or more generative machine-learning models comprises one or more classical machine-learning (CML) models, one or more quantum machine-learning (QML) models, or a combination thereof.
3 . The system of claim 1 , wherein the one or more processors are further configured to uniquely identify the user based at least in part on the generatively presented sequences of different decoy data and the execution of the one or more user interactions with the different decoy data.
4 . The system of claim 1 , wherein the one or more processors are further configured to detect the interaction to initiate the execution of one or more user interactions with the set of sensitive data as corresponding to an adversarial interaction.
5 . The system of claim 1 , wherein the one or more processors are further configured to store the one or more sets of sharded sensitive data as one or more quantum bits (QuBits) of data to a quantum memory of the system.
6 . The system of claim 1 , wherein the one or more sets of sharded sensitive data comprises one of a plurality of sets of sharded sensitive data into which the set of sensitive data is partitioned, and wherein the one or more processors are further configured to:
prior to detecting the interaction to initiate the execution of one or more user interactions with the set of sensitive data, train the one or more generative machine-learning models based at least in part on the plurality of sets of sharded sensitive data.
7 . The system of claim 1 , wherein the one or more processors are further configured to transfer the one or more sets of sharded sensitive data to the trusted database and onto the trusted computing node to transfer the one or more sets of sharded sensitive data to a distinct quorum computing node.
8 . A method, comprising:
detecting an interaction to initiate an execution of one or more user interactions with a set of sensitive data associated with at least one instance of a software application, and, in response:
executing one or more generative machine-learning models trained to generatively present sequences of different decoy data to a user in response to an execution of one or more user interactions with the different decoy data; and
while generatively presenting the sequences of different decoy data to the user:
partitioning the set of sensitive data into one or more sets of sharded sensitive data to be transferred to a trusted database of a predetermined plurality of trusted databases and onto a trusted computing node of a predetermined plurality of trusted computing nodes;
transferring the one or more sets of sharded sensitive data to the trusted database and onto the trusted computing node, wherein the one or more sets of sharded sensitive data is transferred to the trusted database and onto the trusted computing node for obfuscating from view the set of sensitive data; and
in response to determining at least a partial completion of the execution of the one or more user interactions with the different decoy data, storing a log of the generatively presented sequences of different decoy data and the execution of the one or more user interactions with the different decoy data.
9 . The method of claim 8 , wherein the one or more generative machine-learning models comprises one or more classical machine-learning (CML) models, one or more quantum machine-learning (QML) models, or a combination thereof.
10 . The method of claim 8 , further comprising uniquely identifying the user based at least in part on the generatively presented sequences of different decoy data and the execution of the one or more user interactions with the different decoy data.
11 . The method of claim 8 , further comprising detecting the interaction to initiate the execution of one or more user interactions with the set of sensitive data as corresponding to an adversarial interaction.
12 . The method of claim 8 , further comprising:
storing the one or more sets of sharded sensitive data as one or more quantum bits (QuBits) of data to a quantum memory.
13 . The method of claim 8 , wherein the one or more sets of sharded sensitive data comprises one of a plurality of sets of sharded sensitive data into which the set of sensitive data is partitioned, the method further comprising:
prior to detecting the interaction to initiate the execution of one or more user interactions with the set of sensitive data, train the one or more generative machine-learning models based at least in part on the plurality of sets of sharded sensitive data.
14 . The method of claim 8 , further comprising transferring the one or more sets of sharded sensitive data to the trusted database and onto the trusted computing node to transfer the one or more sets of sharded sensitive data to a distinct quorum computing node.
15 . A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to:
detect an interaction to initiate an execution of one or more user interactions with a set of sensitive data associated with at least one instance of a software application, and, in response:
execute one or more generative machine-learning models trained to generatively present sequences of different decoy data to a user in response to an execution of one or more user interactions with the different decoy data; and
while generatively presenting the sequences of different decoy data to the user:
partition the set of sensitive data into one or more sets of sharded sensitive data to be transferred to a trusted database of a predetermined plurality of trusted databases and onto a trusted computing node of a predetermined plurality of trusted computing nodes;
transfer the one or more sets of sharded sensitive data to the trusted database and onto the trusted computing node, wherein the one or more sets of sharded sensitive data is transferred to the trusted database and onto the trusted computing node for obfuscating from view the set of sensitive data; and
in response to determining at least a partial completion of the execution of the one or more user interactions with the different decoy data, store a log of the generatively presented sequences of different decoy data and the execution of the one or more user interactions with the different decoy data.
16 . The non-transitory computer-readable medium of claim 15 , wherein the one or more generative machine-learning models comprises one or more classical machine-learning (CML) models, one or more quantum machine-learning (QML) models, or a combination thereof.
17 . The non-transitory computer-readable medium of claim 15 , wherein the instructions further cause the one or more processors to uniquely identify the user based at least in part on the generatively presented sequences of different decoy data and the execution of the one or more user interactions with the different decoy data.
18 . The non-transitory computer-readable medium of claim 15 , wherein the instructions further cause the one or more processors to detect the interaction to initiate the execution of one or more user interactions with the set of sensitive data as corresponding to an adversarial interaction.
19 . The non-transitory computer-readable medium of claim 15 , wherein the instructions further cause the one or more processors to store the one or more sets of sharded sensitive data as one or more quantum bits (QuBits) of data to a quantum memory.
20 . The non-transitory computer-readable medium of claim 15 , wherein the one or more sets of sharded sensitive data comprises one of a plurality of sets of sharded sensitive data into which the set of sensitive data is partitioned, and wherein the instructions further cause the one or more processors to:
prior to detecting the interaction to initiate the execution of one or more user interactions with the set of sensitive data, train the one or more generative machine-learning models based at least in part on the plurality of sets of sharded sensitive data.