International Business Machines Corporation

Manish Kesarwani

Sameep Mehta

Akshar Kaul

Gagandeep Singh

Hong Min

Krishnasuri Narayanam

Stefano Braghin

Vijay Arya

Ritesh Kumar Gupta

Spyridon Antonatos

Salil Joshi

Prasad G Naldurg

Niels Dominic Pardon

Anton Zorin

Ramya Raghavendra

Diptikalyan Saha

Kalapriya Kannan

Kush Raj Varshney

Prasad Manikarao Deshpande

Mohit Jain

James Willis Pickel

Karthik Sankaranarayanan

John Thomas Richards

Aleksandra Mojsilovic

Darrell Christopher Reimer

Rachel K E Bellamy

Matthew R Arnold

Hima Prasad Karanam

Kaoutar El Maghraoui

Michael James Hind

Sumit Bhatia

Atul S Kumar

Stephanie Houde

Deepak Vijaykeerthy

David John Piorkowski

Rakesh Rameshrao Pimplikar

Suranjana Samanta

Jason Tsay

Hima P Karanan

Jayant R Haritsa

Embodiments for identifying an optimal cloud computing environment for a computing task is disclosed. Embodiments comprises receiving a computing task to be executed in a cloud computing environment, wherein the computing task requires a set of cloud computing environment parameter values of the cloud computing environment, pre-selecting a set of candidate cloud computing environments, each of which meets the set of cloud computing environment parameter values, ranking the candidate cloud computing environments using reward-based ranking parameter values of the candidate cloud computing environments as an additional selection constraint, and selecting the highest ranking cloud computing environment as the optimal cloud computing environment for the computing task. Furthermore, embodiments comprise executing the computing task in the optimal cloud computing environment, monitoring execution when executing the computing task, and updating parameter values of the reward-based ranking for the selected optimal cloud computing environment.

Selecting best cloud computing environment in a hybrid cloud scenario

The method performs at the orchestration interface at which update information, including changes to tasks of a workflow, is received from a task manager system (TMS), where the workflow includes a set of tasks, inputs to the tasks, and outputs from the tasks. The inputs and outputs determine runtime dependencies between the tasks. Based on the update information received, the orchestration interface populates a topology of nodes and edges as a directed acyclic graph (DAG) that maps nodes to tasks and edges to runtime dependencies between tasks, based on node inputs and outputs. The orchestration interface instructs the execution of the tasks and handling dependencies by interacting with a task execution system (TES) and by traversing the DAG, the orchestration interface identifies tasks that depend on completed tasks as per the runtime dependencies and instructs the TES to execute the dependent tasks identified.

Automatically orchestrating a computerized workflow

Methods, systems, and computer program products for providing the status of model extraction in the presence of colluding users are provided herein. A computer-implemented method includes generating, for each of multiple users, a summary of user input to a machine learning model; comparing the generated summaries to boundaries of multiple feature classes within an input space of the machine learning model; computing correspondence metrics based at least in part on the comparisons; identifying, based at least in part on the computed metrics, one or more of the multiple users as candidates for extracting portions of the machine learning model in an adversarial manner; and generating and outputting an alert, based on the identified users, to an entity related to the machine learning model.

Providing the status of model extraction in the presence of colluding users

One embodiment provides a computer implemented method, including: receiving, from a user, a natural language query for data contained within at least one data repository; identifying at least one concept from the natural language query, wherein the at least one concept includes an entity and an intent; identifying a plurality of datasets satisfying the natural language query by querying the at least one data repository utilizing the at least one concept; ranking the dataset based on relevance to the query; generating an extract-transform-load script that extracts, transforms, and loads a dataset selected by the user from the plurality of datasets; and retrieving data included in the dataset utilizing the extract-transform-load script, wherein the retrieving includes returning the data to the user.

Extract-transform-load script generation

Methods, systems, and computer program products for identifying and redacting data from diagnostic operations via monitoring of data are provided herein. A computer-implemented method includes automatically monitoring data, which includes determining which portions of the data are accessed during a diagnostic operation; parsing an input file into portions of parsed data, wherein the input file comprises an initial output of the diagnostic operation; classifying the portions of parsed data into classes by applying at least one of multiple classification models to the parsed data, wherein the at least one classification model is specific to the accessed portions of data; automatically identifying sensitive data in the classified portions of parsed data by applying a class-to-sensitivity mapping technique to the classified portions of parsed data; redacting the identified sensitive data from the input file; and generating and outputting an updated output of the diagnostic operation based on the redacting.

Dynamically identifying and redacting data from diagnostic operations via runtime monitoring of data sources

Methods, systems, and computer program products for generating a data migration plan for in-place encryption of data are provided herein. A computer-implemented method includes receiving, from a user, a request to generate a migration plan for performing in-place encryption of data within a database, wherein the migration plan indicates periods of time in which portions of the data are to be encrypted; determining a set of constraints for performing the in-place encryption; generating the migration plan based at least in part on the set of constraints; and performing the in-place encryption of the data in accordance with the migration plan such that only a single copy of each of the portions is maintained during the in-place encryption, wherein the single copy comprises one of a plaintext copy of data corresponding to the portion, and an encrypted copy of data corresponding to the portion.

Generating data migration plan for in-place encryption of data

One embodiment provides a method, including: receiving, from a user, a dataset for encryption before its storage at a data storage location, wherein the dataset comprises a plurality of portions; identifying (i) attributes of the dataset and (ii) dataset dependencies; generating a recommendation for an encryption scheme to be used for the dataset, wherein the generating comprises (i) generating, based upon the attributes and the dataset dependencies, a recommendation of an encryption scheme for each portion of the dataset and (ii) identifying, based upon the dataset dependencies, a key label for each portion of the dataset, wherein the key label identified for a portion of the dataset that is dependent on another portion of the dataset is the same as the key label identified for said another portion of the dataset; and providing, to the user, (i) the generated recommendation and (ii) a description identifying reasons for the generated recommendation.

Encryption scheme recommendation

A method for automatically generating documentation for an artificial intelligence model includes receiving, by a computing device, an artificial intelligence model. The computing device accesses a model facts policy that indicates data to be collected for artificial intelligence models. The computing device collects artificial intelligence model facts regarding the artificial intelligence model according to the model facts policy. The computing device accesses a factsheet template. The factsheet template provides a schema for an artificial intelligence model factsheet for the artificial intelligence model. The computing device populates the artificial intelligence model factsheet using the factsheet template with the artificial intelligence model facts related to the artificial intelligence model.

Generation and management of an artificial intelligence (AI) model documentation throughout its life cycle

Methods, systems, and computer program products for dynamically analyzing diagnostic operations data via machine learning techniques are provided herein. A computer-implemented method includes defining aspects of machine learning techniques to be performed in connection with diagnostic operation data analysis, including: defining dynamic analysis granularity selection functionality based on time constraints and the level of progress of the analysis; defining dynamic data classification identifier type selection functionality based on the time constraints and the level of progress of the analysis; and defining dynamic ordering of data classification identifiers during runtime based on data classification preferences, information pertaining to system workload, and information pertaining to requested analysis compliance parameters. The method also includes dynamically performing the analysis by applying the machine learning techniques to the diagnostic operation data, and outputting at least a portion of results of the analysis.

Dynamically analyzing diagnostic operations data via machine learning techniques

Methods, systems, and computer program products for generating a data partitioning strategy for secure and efficient query processing are provided herein. A computer-implemented method includes obtaining at least (i) a database schema associated with a database and (ii) a query workload associated with the database; and generating, based at least in part on the database schema and the query workload, a distributed database structure for the database that (i) stores a first set of columns of the database within a private cloud environment in a plaintext format and (ii) stores a second set of columns of the database in a public cloud environment, wherein the second set of columns is stored in either a plaintext format or an encrypted format based on whether the second set of columns comprises sensitive data.

Generating a data partitioning strategy for secure and efficient query processing

Methods, systems, and computer program products for dynamically managing predicate expression columns in an encrypted database are provided herein. A computer-implemented method includes dynamically parsing a query into multiple query predicate expressions; determining that one or more of the query predicate expressions require materialization in an encryption scheme compatible with operations in the predicate expressions; comparing (i) an amount of storage capacity to be consumed by materializing the one or more query predicate expressions to (ii) an amount of available storage budget for materialized columns in one or more databases; upon a determination that the amount of storage capacity to be consumed exceeds the amount of available storage budget, removing at least one existing materialized column from the databases; materializing the query predicate expressions by materializing one or more rows required for execution of the query; and executing the query based on the materialized query predicate expressions.

Dynamically managing predicate expression columns in an encrypted database

One embodiment provides a method, including: receiving, at a data service provider, a request from an information purchaser, wherein the request comprises (i) a budget identifying an amount of money to be spent on information and (ii) an objective function identifying a type of information that the information purchaser is requesting; accessing at least a subset of at least one information set of at least one information seller, wherein each of the at least one information sets comprises an information set available for purchase from the information seller; identifying whether at least one accessed information set that fulfills the received request; and providing, if at least one accessed information set fulfills the received request, a recommendation of an information set for purchase by the information purchaser, wherein the provided recommendation comprises at least one of the identified information sets that fulfills the received request.

Information set purchase recommendations

One embodiment provides a method for delaying malicious attacks on machine learning models that a trained using input captured from a plurality of users, including: deploying a model, said model designed to be used with an application, for responding to requests received from users, wherein the model comprises a machine learning model that has been previously trained using a data set; receiving input from one or more users; determining, using a malicious input detection technique, if the received input comprises malicious input; if the received input comprises malicious input, removing the malicious input from the input to be used to retrain the model; retraining the model using received input that is determined to not be malicious input; and providing, using the retrained model, a response to a received user query, the retrained model delaying the effect of malicious input on provided responses by removing malicious input from retraining input.

Detecting and delaying effect of machine learning model attacks

One embodiment provides a method, including: receiving, from a data owner, an input string of plaintext data comprising a plurality of characters for storage in a database of a third-party storage provider; arranging the plurality of characters of the input string as a half pyramid, wherein the half pyramid comprises a plurality of rows, each row comprising at least one more character than a preceding row; encrypting, using a secure encryption scheme and based upon a key, each row of the half pyramid independently from each other row of the half pyramid; and storing, in the database of the third-party storage provider, the encrypted rows of the half pyramid. Other aspects are claimed and described.

Half-pyramid data encryption

Methods, systems, and computer program products for tracking missing data using provenance traces and data simulation are provided herein. A computer-implemented method includes generating, for each of multiple stages in a data curation sequence, a machine learning model of the data curation sequence, wherein the model is based on historical input records within the data curation sequence, historical output records within the data curation sequence, and provenance data within the data curation sequence; creating a simulated output record based on a detected anomaly corresponding to the data curation sequence; predicting the content of absent input records that precede the simulated output record in the data curation sequence and provenance data corresponding to the simulated output record; and outputting, to a user, in response to a query pertaining to the detected anomaly, the predicted input records and information relating the predicted input records to the detected anomaly.

Growing community of inventors

Bengaluru, India

Manish Kesarwani