SECURE ZERO KNOWLEDGE DATA TRANSFORMATION AND VALIDATION
This specification allows for secure, original data to be attributed, transformed attestably and managed within a data center or distributed across a peer to peer network using zero knowledge proofs that allow the holder of transformed data to verify, with certainty, that their transformed data was derived from the attributed data source, without visibility into the contents of that source data.
1 . A computing device implemented method comprising:
receiving data representing information stored at a node of a distributed network;
analyzing received data in a semantic manner to identify one or more data types reflected in received data;
selecting one or more transforms based on the one or more data types of the received data and data types used by the one or more transforms;
evaluating the received data using the one or more selected transforms to generate an evaluated result; and
providing the evaluated result to the distributed network.
2 . The computing device implemented method of claim 1 , further comprising receiving a data transformation request comprising an address of the received data.
3 . The computing device implemented method of claim 1 , wherein the received data and the evaluated result are cryptographically secured and the one or more selected transforms are digitally signed and authenticated and receiving the data is based on whether the data has been previously validated.
4 . The computing device implemented method of claim 1 , wherein evaluating the received data using the one or more selected transforms comprises encoding an address of the received data into the evaluated result and encoding an address of the one or more selected transforms into the evaluated result.
5 . The computing device implemented method of claim 1 , wherein selecting the one or more transforms comprises comparing a signature representing a jurisdiction of a particular transform with a jurisdiction of the node, wherein selecting the one or more transforms is further based on the comparison.
6 . The computing device implemented method of claim 1 , wherein analyzing the received data in a semantic manner comprises determining one or more sequences of data fields embedded in the received data using a machine learning system trained using historical data.
7 . A system comprising:
a computing device comprising:
a memory configured to store instructions; and
a processor to execute the instructions to perform operations comprising:
receiving data representing information stored at a node of a distributed network;
analyzing received data in a semantic manner to identify one or more data types reflected in received data;
selecting one or more transforms based on the one or more data types and data types used by the one or more transforms;
evaluating the received data using the one or more selected transforms to generate an evaluated result; and
providing the evaluated result to the distributed network.
8 . The system of claim 7 , wherein the processor is configured to execute instructions to perform operations comprising receiving a data transformation request comprising an address of the received data.
9 . The system of claim 7 , wherein the received data and the evaluated result are cryptographically secured and the one or more selected transforms are digitally signed and authenticated and receiving the data is based on whether the data has been previously validated.
10 . The system of claim 7 , wherein evaluating the received data using the one or more selected transforms comprises encoding an address of the received data into the evaluated result and encoding an address of the one or more selected transforms into the evaluated result.
11 . The system of claim 7 , wherein selecting the one or more transforms comprises comparing a signature representing a jurisdiction of a particular transform with a jurisdiction of the node, wherein selecting the one or more transforms is further based on the comparison.
12 . The system of claim 7 , wherein analyzing the received data in a semantic manner comprises determining one or more sequences of data fields embedded in the received data using a machine learning system trained using historical data.
13 . One or more non-transitory computer readable media storing instructions that are executable by a processing device, and upon such execution cause the processing device to perform operations comprising:
receiving data representing information stored at a node of a distributed network;
analyzing received data in a semantic manner to identify one or more data types reflected in received data;
selecting one or more transforms based on the one or more data types and data types used by the one or more transforms;
evaluating the received data using the one or more selected transforms to generate an evaluated result; and
providing the evaluated result to the distributed network.
14 . The system of claim 13 , wherein the processor is configured to execute instructions to perform operations comprising receiving a data transformation request comprising an address of the received data.
15 . The system of claim 13 , wherein the received data and the evaluated result are cryptographically secured and the one or more selected transforms are digitally signed and authenticated and receiving the data is based on whether the data has been previously validated.
16 . The system of claim 13 , wherein evaluating the received data using the one or more selected transforms comprises encoding an address of the received data into the evaluated result and encoding an address of the one or more selected transforms into the evaluated result.
17 . The system of claim 13 , wherein selecting the one or more transforms comprises comparing a signature representing a jurisdiction of a particular transform with a jurisdiction of the node, wherein selecting the one or more transforms is further based on the comparison.
18 . The system of claim 13 , wherein analyzing the received data in a semantic manner comprises determining one or more sequences of data fields embedded in the received data using a machine learning system trained using historical data.