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 performant data matching. Entities maintain large amounts of data and desire to reconcile duplicative records. One way to solve this problem is through data matching. However, standard data matching at the record level can be laborious and inefficient. To remedy these inefficiencies in data matching, the present disclosure describes a system where the token records are tokenized a second time into token sets based on the token records satisfying at least one token set rule. A token set rule may be based on the common presence of multiple tokens in a token record. If multiple token records have the required tokens from the set rule, then those token records can be hashed and rolled-up into the token set (i.e., tokenized a second time into the token set). The token set allows for more efficient data matching.

The present disclosure is directed to a universal data language (UDL) translator. Specifically, the systems and methods disclosed enable input data from a variety of sources to be translated into a UDL that can be consistently analyzed and compared against other sources of data. For example, an entity may upload input data that has a plurality of data terms and definitions (e.g., header column in a spreadsheet). These terms may be duplicative and/or inaccurate with respect to the underlying data. If the entity wishes to compare and transact data within a data marketplace, the entity may not fully comprehend what data it is missing and/or what data another entity may have to offer for trade. To remedy this problem of business semantic management, the present invention discloses steps for creating a UDL and a UDL translator so that any input data can be translated to UDL.

The present disclosure is directed to embodiments directed to systems and methods for policy management. In some implementations, a master policy management system can create a policy template in which all policies of a user can be built, monitored, and enforced. The master policy management system can create a taxonomy for the policy template and receive access and control settings for the policy template from the user. A user can generate policies in the policy template and the master policy management system can review and certify the policies based the accuracy of the policies. Once a policy is built, the master policy management system can review and certify the policy, provide a quality score for the policy, perform lifecycle management, record the policy use, and report alerts regarding the policy.

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 a universal data language (UDL) translator. Specifically, the systems and methods disclosed enable input data from a variety of sources to be translated into a UDL that can be consistently analyzed and compared against other sources of data. For example, an entity may upload input data that has a plurality of data terms and definitions (e.g., header column in a spreadsheet). These terms may be duplicative and/or inaccurate with respect to the underlying data. If the entity wishes to compare and transact data within a data marketplace, the entity may not fully comprehend what data it is missing and/or what data another entity may have to offer for trade. To remedy this problem of business semantic management, the present invention discloses steps for creating a UDL and a UDL translator so that any input data can be translated to UDL.

The present disclosure is directed to systems and methods for reference source matching. Specifically, the systems and methods disclosed enable matching among tokens using a reference source. In one example, a Consolidation Platform may receive tokens from a customer environment and tokens from a reference source environment. The customer tokens may be compared to each other using AB matching. If a match does not occur, the customer tokens may further be compared to the reference source tokens via transitive matching. If a match does occur, then the customer tokens may be denoted as a match. In further example aspects, the reference source may be a universal reference token repository that comprises unique tokens. If, after a match is indicated, the matched token(s) may be compared to the universal reference token repository. If the matched token(s) does not exist, it may be added to the repository for future use.

The present disclosure is directed to systems and methods for predicting and correcting data anomalies. In one example aspect, data is received by the system. The system may analyze the data by profiling the data for certain profiling statistics (e.g., min, max, mean, cardinality, etc.). At least one machine-learning algorithm (e.g., a Random-Forest algorithm) may be applied to the profiled data to identify potential relationships among certain data columns in the data. Once certain relationships are identified, the data that is related may be extracted to form an itemset. A second machine-learning algorithm (e.g., Frequent Pattern Growth algorithm) may be applied to the itemset to identify certain frequencies of related values in the itemset. Low frequency values may indicate anomalies in the dataset. If an anomaly is detected, the system may be configured to provide an intelligent remedial action, such as substituting certain values and/or filling in a missing value.

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.