DCF-informed access control for data fusion in edge environments

1 . A method, comprising:

receiving a data stream from a plurality of data sources of an edge environment, wherein each data source comprises a node of a data confidence fabric including hardware and software based trust insertion technologies;

applying, by the data confidence fabric as the data stream flows through multiple data confidence fabric nodes, a plurality of trust functions comprising at least one hardware based trust insertion technology selected from device signature validation, secure boot using a Trusted Platform Module (TPM), authentication enablement, and provenance generation within a secure enclave, and at least one software based trust insertion technology selected from immutable-storage registration and distributed-ledger registration, wherein each of the trust functions changes a confidence score of the data stream;

identifying the confidence score associated with each data stream received from the plurality of data sources, wherein the confidence score is based at least on the trust functions applied to the data stream as the data stream is ingested into the data confidence fabric;

based on the associated confidence score, determining a confidence level of each data stream received from the plurality of data sources;

selecting the data streams received from the plurality of data sources having a first specified confidence level; and

performing, by a data fusion module, a data fusion process that combines the selected data streams into a fused data stream by aggregating values from the selected data streams and generating fused output data based on the aggregated values, to generate a fused data stream that is an aggregation of the data streams having the first specified confidence level.

2 . The method of claim 1 , wherein the plurality of data sources comprise respective nodes of a data confidence fabric (DCF).

3 . The method of claim 1 , wherein the confidence score concerns performance of hardware and/or software of each of the plurality of data sources.

4 . The method of claim 1 , wherein the confidence level of each of the data streams received from the plurality of data sources is based on a configurable confidence score threshold.

5 . The method of claim 1 , further comprising:

providing the fused data stream to an analysis module for further analysis of the data streams included in the fused data stream.

6 . The method of claim 1 , further comprising:

selecting the data streams received from the plurality of data sources having a second specified confidence level that is lower than the first specified confidence level; and

ensuring that the data streams having the second specified confidence level are not included in the data fusion process.

7 . The method of claim 1 , further comprising:

determining a first weight for the data streams having the first specified confidence level;

selecting the data streams received from the plurality of data sources having a second specified confidence level that is lower than the first specified confidence level;

determining a second weight for the data streams having the second specified confidence level;

taking the first and second weights into account when performing the data fusion process; and

generating the fused data stream that is an aggregation of the data streams having the first specified confidence level and the second specified confidence level.

8 . A computing system comprising:

a processor;

a non-transitory storage medium having stored therein instructions that are executable by the processor that cause the computing system to perform operations comprising:

receiving a data stream from a plurality of data sources of an edge environment, wherein each data source comprises a node of a data confidence fabric including hardware and software based trust insertion technologies;

applying, but the data confidence fabric as the data stream flows through multiple data confidence fabric nodes, a plurality of trust functions comprising at least one hardware based trust insertion technology selected from device signature validation, secure boot using a Trusted Platform Module (TPM), authentication enablement, and provenance generation within a secure enclave, and at least one software-based trust insertion technology selected from immutable- storage registration and distributed-ledger registration, wherein each of the trust functions changes a confidence score of the data stream;

identifying the confidence score associated with each data stream received from the plurality of data sources, wherein the confidence score is based at least on the trust functions applied to the data stream as the data stream is ingested into the data confidence fabric;

based on the associated confidence score, determining a confidence level of each data stream received from the plurality of data sources;

selecting the data streams received from the plurality of data sources having a first specified confidence level; and

performing, by a data fusion module, a data fusion process that combines the selected data streams into a fused data stream by aggregating values from the selected data streams and generating fused output data based on the aggregated values, to generate a fused data stream that is an aggregation of the data streams having the first specified confidence level.

9 . The computing system of claim 8 , wherein the plurality of data sources comprise respective nodes of a data confidence fabric (DCF).

10 . The computing system of claim 8 , wherein the confidence score concerns performance of hardware and/or software of each of the plurality of data sources.

11 . The computing system of claim 8 , wherein the confidence level of each of the data streams received from the plurality of data sources is based on a configurable confidence score threshold.

12 . The computing system of claim 8 , further comprising:

providing the fused data stream to an analysis module for further analysis of the data streams included in the fused data stream.

13 . The computing system of claim 8 , further comprising:

selecting the data streams received from the plurality of data sources having a second specified confidence level that is lower than the first specified confidence level; and

ensuring that the data streams having the second specified confidence level are not included in the data fusion process.

14 . The computing system of claim 8 , further comprising:

determining a first weight for the data streams having the first specified confidence level;

selecting the data streams received from the plurality of data sources having a second specified confidence level that is lower than the first specified confidence level;

determining a second weight for the data streams having the second specified confidence level;

taking the first and second weights into account when performing the data fusion process; and

generating the fused data stream that is an aggregation of the data streams having the first specified confidence level and the second specified confidence level.

15 . A non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations comprising:

receiving a data stream from a plurality of data sources of an edge environment, wherein each data source comprises a node of a data confidence fabric including hardware and software based trust insertion technologies;

applying, by the data confidence fabric as the data stream flows through multiple data confidence fabric nodes, a plurality of trust functions comprising at least one hardware-based trust insertion technology selected from device signature validation, secure boot using a Trusted Platform Module (TPM), authentication enablement, and provenance generation within a secure enclave, and at least one software-based trust insertion technology selected from immutable- storage registration and distributed-ledger registration, wherein each of the trust functions changes a confidence score of the data stream;

identifying the confidence score associated with each data stream received from the plurality of data sources, wherein the confidence score is based at least on the trust functions applied to the data stream as the data stream is ingested into the data confidence fabric;

based on the associated confidence score, determining a confidence level of each data stream received from the plurality of data sources;

selecting the data streams received from the plurality of data sources having a first specified confidence level; and

performing, by a data fusion module, a data fusion process that combines-on the selected data streams into a fused data stream by aggregating values from the selected data streams and generating fused output data based on the aggregated values, to generate a fused data stream that is an aggregation of the data streams having the first specified confidence level.

16 . The non-transitory storage medium of claim 15 , wherein the plurality of data sources comprise respective nodes of a data confidence fabric (DCF).

17 . The non-transitory storage medium of claim 15 , wherein the confidence level of each of the data streams received from the plurality of data sources is based on a configurable confidence score threshold.

18 . The non-transitory storage medium of claim 15 , further comprising:

providing the fused data stream to an analysis module for further analysis of the data streams included in the fused data stream.

19 . The non-transitory storage medium of claim 15 , further comprising:

selecting the data streams received from the plurality of data sources having a second specified confidence level that is lower than the first specified confidence level; and

ensuring that the data streams having the second specified confidence level are not included in the data fusion process.

20 . The non-transitory storage medium of claim 15 , further comprising:

determining a first weight for the data streams having the first specified confidence level;

selecting the data streams received from the plurality of data sources having a second specified confidence level that is lower than the first specified confidence level;

determining a second weight for the data streams having the second specified confidence level;

taking the first and second weights into account when performing the data fusion process; and

generating the fused data stream that is an aggregation of the data streams having the first specified confidence level and the second specified confidence level.