Abstract: One example method includes receiving a request for performance of a cryptographic task, such as the encryption of data, associating a cryptographic standard and a user policy with the cryptographic task, associating a discretionary factor with the cryptographic task, and selecting, based on the cryptographic standard, the user policy, and the discretionary factor, a cryptographic mechanism to perform the cryptographic task. The discretionary factor may specify a time, or window of time, when the cryptographic task should be performed.

Abstract: Energy aware data migration and data replication is disclosed. Data stored in a source storage may be migrated or replicated to a target storage based on a migration time or replication time predicted or estimated by an awareness engine. The awareness engine may include models configured to predict the migration time or the replication time so that the migration time or replication time accounts for a cost and/or source of energy. The migration operation or replication operation is performed at the estimated migration or replication time.

Abstract: One example method includes receiving a request to perform a data process, determining, by a trustworthy energy awareness (TEA) module, based on energy information and user constraints, a best time to perform the data process, and conveying, by the TEA module to a recipient, information indicating the best time to perform the data process. The data process may be a data backup process, or a garbage collection process, for example.

Abstract: One example method includes receiving, by a TEA (trustworthy energy awareness) module, a request from a storage controller to identify a most energy-efficient storage array, as among a plurality of storage arrays, on which to store a set of data, determining, by the TEA module based on energy efficiency information and user constraints, the most energy efficient storage array, and identifying, by the TEA module to the storage controller, the most energy efficient storage array.

Abstract: One example method includes monitoring, by a data confidence fabric (DCF), an energy generator/provider (EGP) to determine whether the EGP is meeting its obligation to provide energy, according to terms of a service level agreement (SLA), to a customer that has executed a smart contract with the EGP, when the DCF determines that the EGP is not meeting the obligation, and depending upon a reason for the EGP failing to do so, lowering, by the DCF, a confidence score of the EGP to generate a reduced confidence score, and recording the reduced confidence score in an immutable ledger.

Abstract: Energy aware data compression is disclosed. A storage array may receive data from a client to be compressed. The storage array may request information from a compression awareness engine that is configured to estimate compression times in the context of energy source and energy cost. The storage array makes a decision to compress the data based on the estimates or response received from the compression awareness engine. The data is then compressed and stored in the storage array.

Abstract: Energy aware data encryption operations are disclosed. An awareness engine includes models configured to generate a recommendation that includes an encryption time and/or an encryption model. When the encryption operation is a data at rest encryption operation, the data is encrypted in accordance with the recommendation and stored at the storage system. When the encryption operation is a data in motion encryption operation, the data is encrypted and transmitted to a target storage system in accordance with the recommendation. At the target storage system, the encrypted may be stored in an encrypted form or decrypted and stored. The recommendations are configured to account for energy considerations including energy cost and/or energy source.

Abstract: Enhancing privacy-preserving data aggregation with a data confidence fabric or confidence scores. Data points ingested into a data confidence fabric are associated with a confidence score. The confidence score is generated as trust insertion technologies are applied to the data points. The confidence score reflects a trustworthiness of the data and reflects how the data points are trusted. When aggregating the data points into aggregated data, the data points are aggregated in a manner that accounts for their confidence scores. This lessens the impact of data points that have comparatively low confidence scores in the aggregated data.

Abstract: One example method includes, in a node of a data confidence fabric (DCF), the node comprising hardware, obtaining, when available, a static confidence score for a lowermost layer of an infrastructure of the node, after a workload is orchestrated to the node, obtaining, when available, a workload confidence score, when the static confidence score and the workload confidence score are available, aggregating the static confidence score and the workload confidence score together to obtain an overall infrastructure confidence score, and applying, by the node, the overall infrastructure confidence score to data passing through the node.

Abstract: One example method includes, for a component of a hardware device, registering a component ID and billing information, creating a data structure that comprises an aggregator node that corresponds to an aggregator, and a node that corresponds to the component, receiving, from a vendor of the component, an acknowledgement as to the accuracy and completeness of the data structure insofar as information in the data structure applies to the component, collecting telemetry concerning operation of the component, and using the telemetry as a basis for performing a process concerning the component and/or operation of the component.

Abstract: Generating policy-based confidence scores for data is disclosed. Data captured by a data confidence fabric is annotated when the data is created, mutated, transited or otherwise handled in the data confidence fabric. The annotations are weighted by a policy to generate policy-based confidence scores. The policy-based confidence scores are used in determining whether the data is sufficiently trusted for use by an application.

Abstract: One example method includes generating respective confidence vectors for respective hops associated with a data confidence fabric (DCF), and each hop is associated with one or more trust insertion technologies, identifying a deviation from one of the confidence vectors, generating a waterfall visualization that identifies a location in the DCF, and a type, of the deviation, automatically identifying a remediation action concerning the deviation, and automatically implementing the remediation action in a node of the DCF.

Abstract: One example method includes determining a spatial distribution of devices in a three dimensional physical environment, determining a polling frequency for each of the devices, receiving data generated by the devices, evaluating the data, and assigning a trust score to the data. The devices may take the form of respective sensors operable to sense a physical attribute of the three dimensional physical environment. A Fourier transform may be employed to determine the polling frequency.

Abstract: A proxy or sidecar configured to connect an application to a data confidence fabric is discloses. The sidecar is configured to perform functions related to the transmission of data. As the sidecar intercepts communications to/from an associated application, the sidecar determines which of several functions to perform related to the transmission of data. In addition, the sidecar ensures that data confidence related operations and trust functions, as specified by the application, are applied to the data. Further, the sidecar identifies lineage when data is changed by an application.

Abstract: Invoking smart contracts in data confidence fabrics is disclosed. Trust functions are applied to data that is ingested into and/or flowing in a data confidence fabric. When a trust function is applied, a smart contract is invoked. The smart contract is configured to publish annotations to a distributed ledger that reflect the confidence information added by the trust function and to perform payment as a service such that the trust provider is paid. Publishing the annotations to a distributed ledger may be coupled or uncoupled to performing payment as a service.

Abstract: Global topics in distributed ledgers are disclosed. A distributed ledger is configured with a global topic whose identifier is publicized and well known. The global topic stores information about trust providers including topic identifiers and smart contract identifiers. Requestors can query the global topic to retrieve the information of specific trust providers. Once the information of the specific trust providers is known, the requestors can publish or write annotations and transactions to the distributed ledger. By associating the entries in the global topic with timestamps, requestors can reconfigure themselves automatically with updated trust provider information whenever a write to the distributed ledger fails.

Abstract: One example method includes connecting a digital wallet of a customer to an agent of a data confidence fabric, receiving, from an application, a request for performance of a trust function, by the data confidence fabric, with respect to customer data, processing the customer data by applying the trust function to the customer data, returning the data, after the processing, to the agent, creating annotations for the customer data, based on the trust function that was applied, transmitting the annotations, and a corresponding trust provider fee amount, to a smart contract, and when the digital wallet of the customer does not have adequate funds to cover the trust provider fee amount, obtaining funds from an alternate source of funding associated with the data confidence fabric.

Abstract: One example method includes connecting a digital wallet of a customer to an agent of a data confidence fabric, receiving, from an application, a request for performance of a trust function, by the data confidence fabric, with respect to customer data, processing the customer data by applying the trust function to the customer data, returning the customer data, after the processing, to the agent, creating annotations for the customer data, based on the trust function that was applied, upon successful execution of a smart contract, transferring a fee from the digital wallet to a trust provider associated with the data confidence fabric, and providing the processed customer data to another entity.

Abstract: One example method includes receiving, by a trust provider from a third party proxy, an indication that the third party proxy has instantiated a smart contract on behalf of the trust provider, connecting wallets of a customer to an agent of a data confidence fabric, in response to a request from an application, providing a trust service that includes processing customer data by applying a trust function to the customer data, creating annotations for the customer data based on the trust function, providing the processed customer data to a recipient, calling the smart contract using successive ones of the customer wallets until the smart contract is successfully executed, transferring, from the customer wallet that resulted in successful execution of the smart contract to the third party proxy, a trust provider fee, and transferring the trust provider fee from a third party proxy wallet to a trust provider wallet.

Abstract: One example method includes receiving a data stream at a node of a data confidence fabric that comprises a group of nodes that are each operable to assign trust metadata to data of the data stream, inspecting the data stream to determine a data type of data in the data stream, accessing a configuration file that applies to all the nodes of the data confidence fabric, and obtaining an equation from the configuration file, mapping the equation to the data, performing a trust insertion process on the data, as specified in the equation, and generating trust metadata that is associated with the data and based on the trust insertion process.