Abstract: Techniques facilitating autonomously rendering an encrypted data anonymous in a non-trusted environment are provided. In one example, a computer-implemented method can comprise generating, by a system operatively coupled to a processor, a plurality of clusters of encrypted data from an encrypted dataset using a machine learning algorithm. The computer-implemented method can also comprise modifying, by the system, the plurality of clusters based on a defined criterion that can facilitate anonymity of the encrypted data.

Abstract: Various embodiments are provided for authenticating an entity in non-secure communication channels via secure out-of-bands channels. An operator may be authenticated to have access to a secured location associated with an entity upon determining the operator retrieved and communicated a unique token, provided by the user and stored at the secured location, back to the user.

Abstract: Embodiments for performing self-contained, consistent data masking in a distributed computing environment by a processor. A data masking operation is performed on one or more datasets in one of a plurality of data formats such that a key of each value of each key-value pair representing a common set of columns or paths for the one or more datasets is masked.

Abstract: A system and method for asset leak risk assessment in blockchains are presented. A risk assessment of recursive call attack vulnerabilities may be cognitively determined according to risk vulnerability measurements generated from a computer program source code, a list of external call functions, a risk assessment function, a list of assets, a parser, or a combination thereof.

Abstract: To improve the security performance of a computer system, a retrieval component executing on at least one hardware processor obtains a list of known compromised passwords. A validation component executing on the at least one hardware processor obtains a specification of a putative password and risk-scores the putative password based at least in part on presence of the putative password in the list of known compromised passwords. The system obtains a specification of an actual password chosen in accordance with the risk score of the putative password. Access to at least one aspect of the computer system is controlled based on the actual chosen password.

Abstract: Quasi-identifiers (QIDs) are detected in a dataset using a set of computing tasks. The dataset has a plurality of records and a set of attributes. An index is generated for the dataset. The index has an indicator for each attribute value of each record in the dataset. Each indicator specifies all the records in the dataset having the same value for the attribute. Each task is assigned an attribute combination and a subset of the plurality of records in the dataset and is passed to a thread for execution on computing resources. The executing task inspects the set of records specified by the index indicator for each attribute value in the attribute combination to produce a result. The result of at least one task identifies a unique record for the associated attribute combination. The attribute combination producing the unique record is a QID.

Abstract: Quasi-identifiers (QIDs) are detected in a dataset using a set of computing tasks. The dataset has a plurality of records and a set of attributes. An index is generated for the dataset. The index has an indicator for each attribute value of each record in the dataset. Each indicator specifies all the records in the dataset having the same value for the attribute. Each task is assigned an attribute combination and a subset of the plurality of records in the dataset and is passed to a thread for execution on computing resources. The executing task inspects the set of records specified by the index indicator for each attribute value in the attribute combination to produce a result. The result of at least one task identifies a unique record for the associated attribute combination. The attribute combination producing the unique record is a QID.

Abstract: Techniques facilitating automatically detecting unauthorized use of sensitive information in content communicated over a network are provided. A computer-implemented method can comprise receiving, by a system operatively coupled to a processor, from a first entity, data associated with the first entity and one or more rules defining use of the data by a second entity. The data and the one or more rules can be defined by the first entity. The computer-implemented method can also comprise analyzing, by the system, content communicated over a network by the second entity to determine whether the content violates the one or more rules. The computer-implemented method can further comprise generating, by the system, information indicative of one or more violations of the one or more rules based on a determination that the content violates the one or more rules.

Abstract: A method of point of interest (POI) load balancing includes storing load profile data of a plurality of POIs, monitoring a position of a mobile device, determining an arrival of the mobile device at a first POI of the plurality of POIs, measuring a duration of stay of the mobile device at the first POI, determining whether the duration of stay of the mobile device at the first POI exceeds a preset time threshold for the first POI, and sending an incentive for a second POI of the plurality of POIs to the mobile device upon the mobile device exceeding the preset time threshold for the first POI.

Abstract: Embodiments for watermarking anonymized datasets using decoys in a computing environment are provided. One or more decoy records may be embedded in an anonymized dataset such that a re-identification attack on the anonymized dataset targets the one or more decoy records.

Abstract: Distributed, consistent utility-preserving data masking is provided by retrieving an original value from a data table; initiating a communication with a mapping service to ascertain whether or not a masking table of the mapping service includes a fictionalized value associated with the original value; when the masking table does not include a fictionalized value associated with the original value, producing a fictionalized value for the original value wherein the fictionalized value preserves at least one utility function of the original value, updating the mapping service to include the fictionalized value in the masking table, and applying a first masking operation by replacing the retrieved original value with the fictionalized value.

Abstract: Embodiments for data anonymity by a processor. A dataset may be transformed into an anonymous dataset by applying a differential privacy operation and a clustering operation to the dataset.

Abstract: A mechanism is provided in a data processing system for extracting subgraphs from a multigraph with approximation. The mechanism receives a multigraph and a relevance function and a pattern generation component executing within the data processing system searches the multigraph for occurrences of at least one pattern based on an approximation function. A relevance computation component executing within the data processing system determines a relevance measure for the multigraph based on the relevance function and the occurrences of the at least one pattern. The mechanism provides output of relevant patterns based on the occurrences of the at least one pattern.

Abstract: Techniques facilitating autonomously rendering an encrypted data anonymous in a non-trusted environment are provided. In one example, a computer-implemented method can comprise generating, by a system operatively coupled to a processor, a plurality of clusters of encrypted data from an encrypted dataset using a machine learning algorithm. The computer-implemented method can also comprise modifying, by the system, the plurality of clusters based on a defined criterion that can facilitate anonymity of the encrypted data.

Abstract: Techniques facilitating autonomously rendering an encrypted data anonymous in a non-trusted environment are provided. In one example, a computer-implemented method can comprise generating, by a system operatively coupled to a processor, a plurality of clusters of encrypted data from an encrypted dataset using a machine learning algorithm. The computer-implemented method can also comprise modifying, by the system, the plurality of clusters based on a defined criterion that can facilitate anonymity of the encrypted data.

Abstract: Techniques for employing natural language processing (NLP) to facilitate geospatial analysis are provided. In one example, a computer-implemented method comprises receiving, by a device operatively coupled to a processor, input information in a natural language format indicating a change to a state of a system comprising different nodes through which physical elements flow, wherein the system is associated with a state model that characterizes movement of the physical elements through the different nodes based on one or more flow parameters. The computer-implemented method further comprises, determining, by the device based on the input information, a subset of the one or more flow parameters associated with the change, determining, by the device based on the input information, one or more constraints to the subset that account for the change, and modifying, by the device, the state model based on the one or more constraints, resulting in a modified state model.

Abstract: Techniques for employing natural language processing (NLP) to facilitate geospatial analysis are provided. In one example, a computer-implemented method comprises receiving, by a device operatively coupled to a processor, input information in a natural language format indicating a change to a state of a system comprising different nodes through which physical elements flow, wherein the system is associated with a state model that characterizes movement of the physical elements through the different nodes based on one or more flow parameters. The computer-implemented method further comprises, determining, by the device based on the input information, a subset of the one or more flow parameters associated with the change, determining, by the device based on the input information, one or more constraints to the subset that account for the change, and modifying, by the device, the state model based on the one or more constraints, resulting in a modified state model.

Abstract: Embodiments for intelligent flow prediction by a processor. One or more flows of a domain of interest between target entities may be forecasted according to one or more forecast models learned via machine learning using extracted features of one or more target variables from one or more data sources. The one or more flows may include a quantitative value, an intensity score, an intensity category, or a combination thereof between the target entities.

Abstract: A method, system and computer program product for providing privacy protection to data streams in a distributed computing environment. The method includes concurrently processing, by a plurality of computer machines, data streams of attributes containing data values received by each of the plurality of local computer machines; indexing the data values for each attribute 1of the plurality of data streams received by each of the plurality of local computer machines; providing the indexed data values to a main computer machine; integrating, by the main computer machine, the local computer machine indexed data values into a global index data structure for the plurality of data streams; and identifying privacy vulnerabilities of the attributes that are direct identifiers and quasi-identifiers based on the global index.

Abstract: A method and system for automatically identifying and protecting privacy vulnerabilities in data streams includes indexing data values for each attribute of the data stream received by local virtual machines based on a schema of each data stream, classifying the data attributes of the plurality of data streams into known data types, integrating the local virtual machine indexes into a global index data structure for the data streams including single attribute data values, identifying privacy vulnerabilities in the data as attributes that are direct identifiers based on the attribute data values stored in the global index and combinations of attributes that are quasi-identifiers based on the low frequency of certain combinations of attribute data value pairs by computing the frequency based on the single attribute data values stored in the global index and providing privacy protection to the data streams by applying data transformations on the discovered direct identifiers and the quasi-identifiers.