Abstract: Systems and methods for the amalgamation of data from different sources for processing by an algorithm is provided. A synthetic data steward receives an encrypted algorithm from the algorithm developer via a core management system. Likewise, two or more data stewards may encrypt their data sets and provide them to the synthetic data steward. The sensitive data being operated upon is compiled into a unified/amalgamated dataset. Within the synthetic data steward is a sequestered computing node. This allows the algorithm and the various sensitive data sets to be decrypted within the sequestered computing node, and computations be performed on the amalgamated data, without the synthetic data steward (or any party for that matter) from having access to the algorithm and/or sensitive data sets while ‘sealed’ in the vault like sequestered computing node.

Abstract: Systems and methods for the selection, verification and recommendation of cohort sample sets is provided. In some embodiments, a dataset selection optimization includes first receiving at data stewards classes of data required by the data consumer. The data stewards process their data (or a subset of their data) into a vector set within a sequestered computing node. These vector sets are transferred to a core management system for minimizing a difference between a target vector and any combination of the data stewards' vector sets. A cost function may also be applied to the vector sets during this optimization. Once the data steward(s) that best match the target vector are identified, they may be placed in contact with the data consumer for access of their information.

Abstract: Systems and methods for the verification of cohort sample sets is provided. In some embodiments, a sample dataset is received, and used to generate a sample vector set. The sample vector is computed by encoding the dataset according to a set of classes, generating a matrix of the encoded dataset (where the rows of the matrix correspond to patients and the columns to a class or subclass), and converting the matrix into a series of vector spaces. An example vector set is received and the difference between the sample vector set and the example vector set. Calculating the difference is by framing the distance as a p-value in a hypothesis test, compared against a threshold. When the p-value is above the threshold the sample dataset is rejected.

Abstract: Systems and methods for data obfuscation are provided. Data obfuscation is needed when protecting an algorithm from reverse engineering attempts. The data is obfuscated by requesting more data from the data steward than is needed by the algorithm. If there are not enough types of data available from the data steward, “low intensity” data types can be requested to fill out the data types requested. These ‘low intensity’ data types are ones that are easily obtained or even regularly collected anyway. The algorithms libraries are altered to call for all the data fields available, thereby rendering reverse engineering extremely difficult, if not impossible.

Abstract: Confirmation of data selection in a zero-trust environment is provided. In some embodiments, a synthetic data steward and/or a traditional data steward can receive the dataset(s). Additionally, a script is received from the algorithm developer. The dataset(s) and script(s) reside within a secure computing node and are therefore inaccessible by any party. The script(s) are executed, resulting in at least one confirmation about the data within the dataset(s). The script(s) complete any of confirming a format for data in the at least one dataset, the expected class values for data within the at least one dataset, an overall characterization and completeness of the at least one dataset, and/or an expected class membership for different data attributes within the at least one dataset.

Abstract: An algorithm is trained on a dataset to facilitate dynamic data exfiltration protection in a zero-trust environment. An inversion threat model using the original training dataset (a ‘gold standard’ inversion model) may also be generated. This inversion model can be characterized to determine its performance/accuracy of properly identifying a given input as being within the original training dataset or not (a data exfiltration event). It is possible to reduce this risk of data exfiltration to a desired level, without unduly impacting the algorithm's performance using the inversion model for the generation of noise that is targeted (as opposed to Gaussian noise). Noise added to the original training dataset causes the inversion model to perform poorer (meaning data steward data is more secure) but has a corresponding impact on the algorithm accuracy and performance.

Abstract: Systems and methods for the quantification of sample set quality is provided. In some embodiments, a sample dataset and a sample vector set are received. A rule-based screening of the sample dataset is applied to generate a heuristic quality score. Additionally, a sample vector set is generated from the sample dataset. The difference between the sample vector set and the example vector set is calculated to generate a degree of difference quality score. The heuristic quality score and the degree of difference quality score are normalized and then combined into a quality metric. Calculating the difference between the sample vector set and the example vector set is by framing the distance as a p-value in a hypothesis test, compared against a threshold.

Abstract: Systems and methods for recommendation of cohort sample sets is provided. In some embodiments, a set of dataset requirements is received as a required vector set. The historical vector sets are queried. Each vector set corresponds to a known dataset. The difference between the required vector set and each of the historical vector sets is calculated by framing the distance as a p-value in a hypothesis test, compared against a threshold. The historical vector set with the least difference to the required vector set is identified. The least difference is calculated as a count of differing classes or as a numerically weighted summation of differing classes.

Abstract: Systems and methods for the deployment and operation of an algorithm in a zero-trust environment are provided. In some embodiments, an algorithm is encrypted by an algorithm developer within a zero-trust computing node, using a public key. This generates a payload that is transferred to a core management system which in turn distributes the payload to one or more sequestered computing nodes located within the infrastructure of one or more data stewards. The sequestered computing nodes are designed to preserve privacy of data assets and the algorithm. Next the payloads are decrypted, using a private key, within the sequestered computing nodes. This yields the algorithm that can be run against the data assets of the data steward. A report is generated that can be shared with the appropriate parties.

Abstract: Systems and methods for the processing of diverse datasets by divergent algorithms within a zero-trust environment is provided. In some embodiments, a single data steward may receive multiple algorithms in a zero-trust environment. Alternatively, algorithm output may be obfuscated for sharing with the algorithm developer for validation, or to compare against the output of a different data steward's processed protected information, for example PHI. In such situations the hashed identifying information may be matched using AI models. In yet other embodiments, the output of one data steward's protected information may be provided in a zero-trust manner to the sequestered enclave of a second data steward in order to impact the processing of this second data steward's protected information by a second algorithm.