Collibra Belgium BV

The present disclosure is directed to systems and methods for securely reflecting data from a local datastore. In one example embodiment, a user may connect local datastores to edge nodes that may facilitate communication between a third-party data management hub and the local datastores. A user operating the third-party data management hub may request to read/write certain data at the local datastores (that the user owns). Data from the local datastore may be generated in response to the request. The responsive data may be transmitted over an encrypted data session to the data management hub, where the responsive data is reflected to the user on a user interface. The responsive data is stored in a volatile memory store and not moved from the persistent memory at the local datastore. Upon termination of the encrypted data session, the volatile memory store is erased to preserve the privacy/security of the underlying data.

The present disclosure is directed to systems and methods for securely managing and administering an encryption/decryption key using distributed ledger technology (DLT). In some examples, a client may possess a data attribute (or a dataset of data attributes). The client may receive tokenization parameters to apply to the data attribute to encrypt the data attribute. After tokenizing the data attribute, the client may then request the creation of an encryption key to be applied to the token. A third-party key management system (KMS) may create an encryption key and a salt. The salt may be applied to the token, and the salted token may then be encrypted. Additionally, a decryption key may be created and stored securely at the third-party KMS. The client may transmit the encrypted token to a third-party consolidation platform, wherein the consolidation platform requests access to the decryption key to unveil the underlying token.

The present disclosure relates to methods and systems for contextual data masking and registration. A data masking process may include classifying ingested data, processing the data, and tokenizing the data while maintaining security/privacy of the ingested data. The data masking process may include data configuration that comprises generating anonymized labels of the ingested data, validating an attribute of the ingested data, standardizing the attribute into a standardized format, and processing the data via one or more rules engines. One rules engine can include an address standardization that generates a list of standard addresses that can provide insights into columns of the ingested data without externally transmitting the client data. The masked data can be tokenized as part of the data masking process to securely maintain an impression of the ingested data and generate insights into the ingested data.

The present disclosure is directed to systems and methods for securely managing and administering an encryption/decryption key using distributed ledger technology (DLT). In some examples, a client may possess a data attribute (or a dataset of data attributes). The client may receive tokenization parameters to apply to the data attribute to encrypt the data attribute. After tokenizing the data attribute, the client may then request the creation of an encryption key to be applied to the token. A third-party key management system (KMS) may create an encryption key and a salt. The salt may be applied to the token, and the salted token may then be encrypted. Additionally, a decryption key may be created and stored securely at the third-party KMS. The client may transmit the encrypted token to a third-party consolidation platform, wherein the consolidation platform requests access to the decryption key to unveil the underlying token.

The present disclosure is directed to systems and methods for generating synthetic data. Entities maintain large amounts of data, and conducting probability distribution and/or correlation analyses on these large datasets while maintaining data privacy for personally identifiable information (PII) is difficult. The present application describes methods for identifying data fields that comprise PII and synthesizing the data so that the PII is removed, but the integrity of the probability distribution and/or correlation metrics remains. Certain data is grouped into data fields based on a data table type, and each data type may be assigned a certain data analysis strategy, which may comprise a joint probability distribution, a characterbase data faker, a genetic regex generator, and/or a timeseries model. A table sketch may be generated that may comprise at least one synthesizer recipe to be used in future data queries.

The present disclosure is directed to systems and methods for securely managing and administering an encryption/decryption key using distributed ledger technology (DLT). In some examples, a client may possess a data attribute (or a dataset of data attributes). The client may receive tokenization parameters to apply to the data attribute to encrypt the data attribute. After tokenizing the data attribute, the client may then request the creation of an encryption key to be applied to the token. A third-party key management system (KMS) may create an encryption key and a salt. The salt may be applied to the token, and the salted token may then be encrypted. Additionally, a decryption key may be created and stored securely at the third-party KMS. The client may transmit the encrypted token to a third-party consolidation platform, wherein the consolidation platform requests access to the decryption key to unveil the underlying token.

The present disclosure relates to methods and systems for contextual data masking and registration. A data masking process may include classifying ingested data, processing the data, and tokenizing the data while maintaining security/privacy of the ingested data. The data masking process may include data configuration that comprises generating anonymized labels of the ingested data, validating an attribute of the ingested data, standardizing the attribute into a standardized format, and processing the data via one or more rules engines. One rules engine can include an address standardization that generates a list of standard addresses that can provide insights into columns of the ingested data without externally transmitting the client data. The masked data can be tokenized as part of the data masking process to securely maintain an impression of the ingested data and generate insights into the ingested data.

The present disclosure is directed to systems and methods for enriching data. Specifically, the systems and methods disclosed enable the enrichment of data via matching, identifying composite data records, and utilizing Reference Source datasets. In one example aspect, Customer data is tokenized and then subsequently transmitted to a third-party Consolidation Platform. The Customer tokens may comprise multiple token records, wherein the multiple token records are displayed in the form of a bitmap. The bitmap may indicate which attributes in a Customer record may be present or absent. The composited Customer token records may then be matched to a Reference Source token set, wherein the matching analysis identifies missing data attributes in the Customer token set that the Customer may or may not already possess. The missing data attributes may be populated and/or updated in a Customer environment based on the Reference Source token set. In other example aspects, a data quality score may be assigned to each data attribute.

The present disclosure relates to methods and systems for contextual data masking and registration. A data masking process may include classifying ingested data, processing the data, and tokenizing the data while maintaining security/privacy of the ingested data. The data masking process may include data configuration that comprises generating anonymized labels of the ingested data, validating an attribute of the ingested data, standardizing the attribute into a standardized format, and processing the data via one or more rules engines. One rules engine can include an address standardization that generates a list of standard addresses that can provide insights into columns of the ingested data without externally transmitting the client data. The masked data can be tokenized as part of the data masking process to securely maintain an impression of the ingested data and generate insights into the ingested data.

The present disclosure is directed to systems and methods for securely managing and administering an encryption/decryption key using distributed ledger technology (DLT). In some examples, a client may possess a data attribute (or a dataset of data attributes). The client may receive tokenization parameters to apply to the data attribute to encrypt the data attribute. After tokenizing the data attribute, the client may then request the creation of an encryption key to be applied to the token. A third-party key management system (KMS) may create an encryption key and a salt. The salt may be applied to the token, and the salted token may then be encrypted. Additionally, a decryption key may be created and stored securely at the third-party KMS. The client may transmit the encrypted token to a third-party consolidation platform, wherein the consolidation platform requests access to the decryption key to unveil the underlying token.