Abstract: An example system includes a processor to receive a graph-based masking policy and a composite payload containing a data object to be masked. The processor is to instantiate a masking engine based on the graph-based masking policy. The processor is to execute the masking engine on the composite payload to generate a masked payload comprising a masked data object. The data object to be masked is masked in place such that the resulting composite payload type is maintained. The processor is to output the masked payload.

Abstract: A method, computer system, and a computer program product for personal data discovery is provided. The present invention may include determining at least one feature used to train a target machine learning (ML) model. The present invention may also include mapping the determined at least one feature to at least one location of a data store including at least one personal data associated with the determined at least one feature. The present invention may further include retrieving a data record of the at least one personal data associated with the mapped at least one feature from the at least one location of the data store. The present invention may also include determining that the target ML model includes a trace of the retrieved data record. The present invention may further include marking the target ML model as containing the at least one personal data.

Abstract: The example embodiments are directed to a system and method for managing blockchain transaction processing. In an example, the method includes one or more of receiving a message transmitted from a client device, the message including a predefined structural format for processing by a service providing computing system, determining a type of the message and detecting one or more sensitive fields within the message based on the determined type of the message, anonymizing values of the one or more sensitive fields within the message while leaving the predefined structural format intact, and transmitting the anonymized message including the one or more anonymized values with the predefined structural format remaining intact to the service providing computing system. The system can anonymize data from a private network before it is transmitted to a public service.

Abstract: A method, computer system, and a computer program product for data processing, comprising obtaining a plurality of files from a data source. These files are analyzed the files for information about the content and in order to determine structural information of each file. Once the files have been analyzed, information in each file may be sorted and categorized by common content. Sensitive information may also be extracted and categorized separately. Information may then be then merged using the categories to create a single unified file.

Abstract: An example system includes a processor to receive training data and predictions on the training data of a trained machine learning model to be anonymized. The processor is to generate generalized data from training data based on the predictions of the trained machine learning model on the training data. The processor is to train an anonymized machine learning model using the generalized data.

Abstract: Embodiments of the present systems and methods may provide techniques to distinguish between data categories. For example, a method implemented in a computer system may comprise obtaining, at the computer system, a plurality of data strings in different categories, each category having a same string pattern, determining a loose string format and a set of restrictions based on at least one string pattern, classifying the plurality of data strings to respective different categories based on a loose string format of the data strings and on the restrictions on the data strings of the different categories using a classification score indicating utilizing restriction information of other categories when determining the matching of a category, and decreasing the classification score if a mean restriction matching proportion is not part of a category or is a threshold amount above an expected mean restriction matching proportion.

Abstract: An example system includes a processor to receive a graph-based masking policy and a composite payload containing a data object to be masked. The processor is to instantiate a masking engine based on the graph-based masking policy. The processor is to execute the masking engine on the composite payload to generate a masked payload comprising a masked data object. The data object to be masked is masked in place such that the resulting composite payload type is maintained. The processor is to output the masked payload.

Abstract: An example system includes a processor to receive an instance of a composite format comprising a masking restriction. The processor can generate a mask for the instance of the composite format based on the masking restriction. The processor can output the generated mask.

Abstract: An example system includes a processor to receive an instance of a format and a masking restriction. The processor can rank the instance of the format to generate an integer in an effective domain of the format. The processor can apply noise to the integer based on the masking restriction to generate a perturbed integer. The processor can unrank the perturbed integer to generate a second instance of the format.

Abstract: An example system includes a processor to receive a valid instance of a finite regular expression format. The processor is to generate a state machine corresponding to the finite regular expression format. The processor is to recursively compute a number of matched strings for each state and transition in the generated state machine. The processor is to recursively rank the valid instance of the finite regular expression format using the generated state machine with the computed numbers of matched strings. The processor is to output a number rank for the valid instance of the finite regular expression format.

Abstract: An example system includes a processor to receive, at a setup or sign-up, a first cipher including a biometric template transformed using a first transformation and encrypted using a secret key, a second cipher including a security vector encrypted using the secret key, a third cipher including the biometric template transformed using a second transformation and encrypted, and a fourth cipher including an encrypted second security vector. The processor can receive, at a runtime or sign-in, a fifth cipher and a sixth cipher. The processor can verify that the fifth cipher includes a second biometric template transformed using the first transformation and encrypted using the secret key and that the sixth cipher includes the second biometric template transformed using the second transformation by testing a format attribute of the transformation functions using comparisons of inner products.

Abstract: An example system includes a processor to generate regular expressions representing textual pattern facets of sub-formats of a composite format, and a regular expression representing a composite textual pattern of the composite format based on sub-format and composition type. The processor can search the data using generated regular expression representing composite textual patterns to detect occurrences of candidate matches. The processor can recursively match and validate the detected occurrences with the composite format and hierarchically match and validate sub-formats in the detected occurrence. The processor can mask in place the detected occurrence of the composite format in the data using ranking-based integer format preserving masking.

Abstract: A method, computer system, and a computer program product for testing a data removal are provided. Data elements are marked with a respective mark per represented entity. The marked data elements, with labels indicating the respective marks, are input into a machine learning model to form a trained machine learning model. The trained machine learning model is configured to perform a dual task that includes a main task and a secondary task that includes a classification based on the labels. A forgetting mechanism is applied to the trained machine learning model to remove a data element including a test mark of the marked data elements. A test data element marked with the test mark is input into the revised machine learning model. The classification of the secondary task of an output of the revised machine learning model is determined for the input test data element.

Abstract: A method for forgetting data samples from a pretrained neural network (NN) model is provided. The method includes training an adversarial model to classify training data samples as members of the NN model and test data samples as non-members of the NN model. The method includes performing the following iteratively until the NN model has forgotten a specified threshold of data samples to be forgotten: (1) classifying the data samples as members or non-members using the trained adversarial model; (2) for the member data samples, determining a subset that includes data samples to be forgotten; (3) labeling the data samples within the subset as non-members and updating the NN model based on weight update techniques that cause the NN model to forget the data samples; (4) retraining the NN model without the data samples that have been forgotten; and (5) retraining the adversarial model for the next iteration.

Abstract: A method, computer system, and a computer program product for personal data discovery is provided. The present invention may include determining at least one feature used to train a target machine learning (ML) model. The present invention may also include mapping the determined at least one feature to at least one location of a data store including at least one personal data associated with the determined at least one feature. The present invention may further include retrieving a data record of the at least one personal data associated with the mapped at least one feature from the at least one location of the data store. The present invention may also include determining that the target ML model includes a trace of the retrieved data record. The present invention may further include marking the target ML model as containing the at least one personal data.

Abstract: Embodiments may include techniques to prevent illegal ciphertexts using distance computations on homomorphic and/or functional encrypted templates while detecting whether the resulting distance does not meet requirements for validity.

Abstract: Embodiments may provide distance computations on homomorphic and/or functional encrypted vectors while detecting whether the resulting distance has wrapped around due to the vectors having elements not in an allowed range. A method of user authentication processing may comprise receiving and storing enrollment information from a client computer system, the enrollment information comprising a template of authentication data and at least one additional encrypted vector, receiving an additional template to be used to authenticate the user from the client computer system, authenticating the user using the received additional template using the stored template and the stored at least one additional encrypted vector, and determining that authentication is successful when the received additional template matches the stored template and is valid based on the stored at least one additional encrypted vector.

Abstract: An example system includes a processor to receive training data and predictions on the training data of a trained machine learning model to be anonymized. The processor is to generate generalized data from training data based on the predictions of the trained machine learning model on the training data. The processor is to train an anonymized machine learning model using the generalized data.

Abstract: A method, apparatus and a product for data generalization for predictive models. The method comprising: based on a labeled dataset, determining a plurality of buckets, each of which has an associated label; determining a plurality of clusters, grouping similar instances in the same bucket; based on the plurality of clusters, determining an alternative set of features comprising a set of generalized features, wherein each generalized feature corresponds to a cluster of the plurality of clusters, wherein a generalized feature that corresponds to a cluster is indicative of the instance being mapped to the corresponding cluster; obtaining a second instance; determining a generalized second instance that comprises a valuation of the alternative set of features for the second instance; and based on the generalized second instance, determining a label for the second instance.

Abstract: Embodiments of the present systems and methods may provide techniques for verifying the correct application purpose for applications that serve multiple purposes and to determine the correct purpose for each requested data access. For example, in an embodiment, a method for controlling application access to data implemented in a computer comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor may comprise: receiving an application comprising a plurality of application parts, each application part associated with a declared data access purpose and generating a cryptographic certificate for each application part to be certified by determining whether a declared data access purpose for each application part to be certified is correct and the only data access purpose for that part, wherein the declared purpose is included in purpose information associated with each application part to be certified.