Abstract: Methods, systems and computer program products for root cause analysis using provenance data are provided herein. A computer-implemented method comprises computing a plurality of provenance paths for at least one of a plurality of data elements in a curation flow and a plurality of groups of data elements in the curation flow, analyzing the computed provenance paths to determine one or more errors in the curation flow, and outputting the one or more errors in the curation flow to at least one user. The analyzing comprises at least one of identifying which of the computed provenance paths are partial provenance paths, and identifying one or more output records associated with the curation flow, wherein the one or more output records comprise incorrectly curated data, and identifying the computed provenance paths that respectively correspond to the one or more output records comprising the incorrectly curated data.

Abstract: Methods, systems, and computer program products for generating collaborative orderings of information pertaining to products to present to target users are provided herein.

Abstract: One embodiment provides a method, including: receiving, at a third-party storage provider and from a data owner, a plurality of encrypted documents, wherein each of the plurality of encrypted documents is encrypted by the data owner using at least one encryption key; receiving, from a query user, an encrypted query, wherein the query is encrypted using the at least one encryption key; computing an edit distance value between the encrypted query and at least a portion of the plurality of encrypted documents, wherein the computing comprises communicating with an entity to work together to compute the edit distance value; the communicating comprising (i) providing, from the third-party storage provider to the entity, an encrypted function of an edit distance matrix and (ii) receiving an encrypted edit distance value computed by the entity from the encrypted function; and returning the encrypted edit distance value to the query user.

Abstract: One embodiment provides a method, including: receiving a query from a user requesting assistance regarding instructions for performing a task; identifying, within steps of the instructions, words that can be visualized, wherein the identifying comprises identifying relationships between terms within the query to generate a step query; retrieving, for each of the steps, a plurality of images representing the identified words; identifying at least one object occurring within the plurality of images corresponding to more than one of the steps; selecting an image for each of the steps of the instructions, wherein the selecting an image comprises selecting an image for each step such that the identified at least one object is represented similarly in each selected image including the identified at least one object; and presenting the instructions as visualized instructions by presenting the selected images for each of the steps in order.

Abstract: Methods, systems, and computer program products for assessing value of one or more data sets in the context of a set of applications are provided herein. A computer-implemented method includes selecting analytic applications of interest based on a characterization of data attributes of each of the available data sets; automatically determining an impact of each of the data attributes of each of the available data sets on an end value of each of the analytic applications of interest; automatically computing an amount of improvement to the end value of each of the analytic applications of interest based on inclusion of an additional data set; and automatically determining a value attributed to the additional data set based on a comparison of (i) the cost of adding the additional data set to the available data sets to (ii) the computed amount of improvement based on the inclusion of the additional data set.

Abstract: Asset recommendation for a particular input dataset is provided. Candidate data analysis assets having a corresponding relatedness score associated with the particular input dataset greater than a defined relatedness score threshold value are selected. Those candidate data analysis assets having a corresponding relatedness score greater than the defined relatedness score threshold value are ranked by score. Those candidate data analysis assets having a corresponding relatedness score greater than the defined relatedness score threshold value are listed by rank from highest to lowest. A justification for each candidate data analysis asset is inserted in the ranked list of candidate data analysis assets. The ranked list of candidate data analysis assets along with each respective justification is outputted on a display device.

Abstract: A processor may receive plaintext data. The plaintext data may correspond to a query. The processor may identify a granularity of the plaintext data. The processor may compress the plaintext data using a binary search tree. The binary search tree may compress the plaintext data based on the granularity of the plaintext data. The processor may encrypt the plaintext data by randomizing the order in which the binary search tree stores the compression of the plaintext data. The stored order of the binary search tree may act as a private key. The processor may process the query over an encrypted cumulative compressed database.

Abstract: 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.

Abstract: Methods, systems and computer program products for association rule mining of an encrypted database are provided herein. A computer-implemented method includes receiving, at a first cloud computing environment, encrypted transaction data that are encrypted using an encryption scheme which provides additive homomorphism, wherein the transaction data comprise a plurality of combinations of two or more elements of a set of elements, receiving, at the first cloud computing environment, encrypted query data that are encrypted using the encryption scheme, wherein the query data comprise at least one of an element and a combination of two or more elements of the set of elements which are the subject of a query seeking a determination of whether at least one of the element and the combination of two or more elements is frequent, and computing addition of the encrypted query data with the encrypted transaction data.

Abstract: Methods, systems, and computer program products for secure distance computations are provided herein.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: One embodiment provides a method, including: deploying a machine learning model, wherein the deployed machine learning model is used in responding to queries from users; receiving, at the deployed machine learning model, input from a user; identifying a type of machine learning model attack corresponding to the received input; computing, responsive to receiving the input, a resiliency score of the machine learning model, wherein the resiliency score indicates resistance of the machine learning model against the identified type of attack; and performing an action responsive to the computed resiliency score.

Abstract: Methods, systems, and computer program products for enabling distance-based algorithms on data encrypted using a 2DNF homomorphic encryption scheme with inefficient decryption are provided herein. A computer-implemented method includes generating multiple versions of a data point, wherein each of the multiple versions of the data point comprises a distinct value corresponding to a distinct Euclidean space; encrypting each of the multiple versions of the data point; storing the multiple encrypted versions of the data point across multiple databases; and executing one or more distance-based algorithms on the multiple encrypted versions of the data point by using a finite decryption table across the multiple databases, wherein the finite decryption table stores a set of plaintext-ciphertext mappings between (i) multiple plaintext values and (ii) multiple encrypted ciphertext values corresponding to the multiple plaintext values.

Abstract: One embodiment provides a method, including: receiving, at a third-party storage provider, (i) a plurality of encrypted documents and (ii) a plurality of encrypted vectors corresponding to the plurality of encrypted documents; receiving a request to search the plurality of encrypted documents using an encrypted query vector; identifying whether at least one encrypted document from the encrypted documents is determined to be similar to the plaintext document provided in the received request, wherein the determining a similarity comprises communicating, between the third-party storage provider and at least another third-party storage provider without communicating components that would allow the other third-party storage provider to derive information regarding the documents; and returning a plaintext version of a returned encrypted document determined to be similar to the plaintext document provided in the received request.

Abstract: One embodiment provides a method, comprising: receiving a plurality of jobs for processing, wherein each of the plurality of jobs comprises a plurality of tasks and wherein at least one of the plurality of jobs is dependent on another of the plurality of jobs; receiving task dependencies between tasks of the at least one of the plurality of jobs and tasks of the another of the plurality of jobs, wherein the task dependencies identify dependent tasks from the tasks of the at least one of the plurality of jobs and dependee tasks from the tasks of the another of the plurality of jobs; scheduling the processing of the dependent tasks as being based upon only the completed processing of the dependee tasks; and performing job processing of the dependent tasks after processing of the dependee tasks irrespective of the overall job processing status of the another of the plurality of jobs.

Abstract: Methods, systems, and computer program products for schema-free in-graph indexing are provided herein. A computer-implemented method includes creating multiple indexes directed to data within a knowledge graph; correlating two or more of the created indexes, thereby generating one or more multi-dimensional indexes; determining, based on a received query, one or more traversal paths within the data of the knowledge graph and the generated multi-dimensional indexes, wherein the traversal paths facilitate processing of the query; and outputting a response to the query based on the determined traversal paths.

Abstract: One embodiment provides a method, including: assigning a machine learning model signature to a machine learning model, wherein the machine learning model signature is generated using (i) data points and (ii) corresponding data labels from training data; receiving input comprising identification of a target machine learning model; acquiring a target signature for the target machine learning model by generating a signature for the target machine learning model using (i) data points from the assigned machine learning model signature and (ii) labels assigned to those data points by the target machine learning model; determining a stolen score by comparing the target signature to the machine learning model signature and identifying the number of data labels that match between the target signature and the machine learning model signature; and classifying the target machine learning model as stolen based upon the stolen score reaching a predetermined threshold.

Abstract: Methods, systems, and computer program products for multi-level colocation and analytical processing of spatial data on MapReduce are provided herein. A method includes correlating multiple items of spatial data and multiple items of attribute data within a file system to generate multiple blocks of correlated data; colocating each of the multiple blocks of correlated data on a given node within the file system based on a data block placement policy; and clustering multiple replicas generated for each of the multiple data blocks at multiple levels of spatial granularity within the file system.