International Business Machines Corporation

Ariel Farkash

Micha Gideon Moffie

Abigail Goldsteen

Igor Gokhman

Omri Soceanu

Boris Rozenberg

Ron Shmelkin

Allon Adir

Ehud Aharoni

Patrick Ryan Wardrop

Lev Greenberg

Brian Robert Matthiesen

Gilad Ezov

Ilgen Banu Yuceer

Matthew Elsner

David G Druker

Ron Ben-Natan

Ayman Jarrous

Sigal Asaf

Tamar Domany

Ksenya Kveler

Muhammad Barham

Eitan Hadar

Mattias Marder

Tamer Salman

Ury Segal

Dafna Sheinwald

John T E Timm

Amnon Ribak

Dov Murik

Nadav Yosef Har'el

Ohad Greenshpan

Michael Vinov

Dorit Baras

Edward Vitkin

Yehoshua Sagron

Sivan Rabinovich

Yuval Hager

Holly Connor

Gal Peretz

Igor Gorkhman

An example system includes a processor to automatically generate a regular expression automaton matching a textual pattern of a received format. The regular expression automaton is a superset of the format. The processor can automatically generate a multi-dimensional filter to be applied to input data based on the generated regular expression automaton.

Data classification using dynamically filtered formats

A method, a structure, and a computer system for expanding database column names. Exemplary embodiments may include identifying glossary terms that sufficiently syntactically match a column name, extracting partial expansions from the glossary terms, selectively combining a set of the partial expansions, and storing the combined set of partial expansions in association with the column name.

Expanding database column names

An example system includes a processor to receive a value formatted using a composite subtraction format. The processor can determine an interval of a number of intervals that corresponds to the received value using minuend rank. The processor can then calculate rank of the value based on the position of the value within the determined interval.

Ranking instances of composite subtraction format using ranked intervals

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.

Searching, matching, and masking of composite formats

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.

Generating masks for formats including masking restrictions

A method, apparatus and a product for data generalization for predictive models. The method comprising: obtaining a training dataset that comprises a plurality of training instances and predicted labels thereof, wherein each training instance is a valuation of a set of features, wherein the set of features comprises a feature having a domain, wherein the predicted label of each training instance is a label predicted thereto by a predictive model; training an auxiliary model using the training dataset; based on the auxiliary model, determining an alternative set of features that is a generalization of the set of features, wherein the alternative set of features comprises a generalized feature having a generalized domain, wherein each value in the generalized domain corresponds to one or more values in the domain; obtaining a generalized instance having a valuation of the alternative set of features; and determining a label for the generalized instance.

Data generalization for predictive models

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.

Masking composite payloads using policy graphs

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.

Data sanitization system for public host platform

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.

Discovery of personal data in machine learning models

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.

Training anonymized machine learning models via generalized data generated using received trained machine learning models

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.

Exploitation of domain restrictions for data classification

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. The processor can authenticate a user based on the Euclidean distance between the first cipher and the sixth cipher not exceeding a threshold in response to detecting that the transformations are verified.

Authentication using transformation verification

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. For example, a method may comprise receiving and storing enrollment information from a client computer system, the enrollment information comprising an encrypted enrollment template of authentication data and a plurality of randomly generated encrypted templates, receiving an additional encrypted template to be used to authenticate the user from the client computer system, authenticating the user by determining distances between the received additional encrypted template and the stored encrypted enrollment template and the each of the stored plurality of randomly generated encrypted templates, and determining that authentication is successful when the received additional encrypted template meets distance requirements relative to the stored encrypted enrollment template and the each of the stored plurality of randomly generated encrypted templates.

Preventing malformed ciphertext attacks on privacy preserving biometric authentication

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.

Ensure valid range of values of a vector for distance calculations using homomorphic encryption or functional encryption

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.

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Shimshit, Israel

Ariel Farkash