Abstract: A method alters a computer resource in response to the computer resource moving from a first geolocation to a second geolocation. One or more processors receive a message indicating that a computer resource has moved from a first geolocation to a new geolocation. In response to receiving the message that the computer resource has moved from the first geolocation to the new geolocation, the processor(s) encrypt data that is stored on the computer resource, and apply decryption information to the encrypted data from the new geolocation, where the decryption information is specifically for decrypting encrypted data at the new geolocation. In response to the decryption information failing to decrypt the encrypted data at the new geolocation, the processor(s) and/or a user alter the computer resource.

Abstract: An example operation may include one or more of retrieving a snapshot of key values of a world state database, retrieving a hash of the snapshot from a blockchain associated with the world state database, determining whether the snapshot is valid based on the hash of the snapshot retrieved from the blockchain, and in response to determining the snapshot is valid, updating key values of a world state database based on the snapshot of key values.

Abstract: Anomalous control and data flow paths in a program are determined by machine learning the program's normal control flow paths and data flow paths. A subset of those paths also may be determined to involve sensitive data and/or computation. Learning involves collecting events as the program executes, and associating those event with metadata related to the flows. This information is used to train the system about normal paths versus anomalous paths, and sensitive paths versus non-sensitive paths. Training leads to development of a baseline “provenance” graph, which is evaluated to determine “sensitive” control or data flows in the “normal” operation. This process is enhanced by analyzing log data collected during runtime execution of the program against a policy to assign confidence values to the control and data flows. Using these confidence values, anomalous edges and/or paths with respect to the policy are identified to generate a “program execution” provenance graph associated with the policy.

Abstract: Aspects of the present disclosure relate to an autonomous mobile attenuator system for mitigating vehicular collisions. The system includes one or more mobile attenuators that receive data indicating a need for deployment from one or more sensors. The one or more mobile attenuators perform a collision risk assessment on the received data to determine a probability of a potential vehicle collision. The one or more mobile attenuators determine the probability of the potential vehicle collision exceeds a predetermined risk threshold value. The one or more mobile attenuators determine a predicted location for the potential vehicle collision. The one or more mobile attenuators proceed to the predicted location to mitigate the potential vehicle collision.

Abstract: A method contains malware within a network resource. A blockchain system establishes a smart contract, on the blockchain system, for a network resource in a computer environment. The smart contract is for an action to be performed on the network resource if a malware is detected on the network resource. In response to malware being detected in the network resource, the blockchain system determines whether a consensus is reached by a plurality of computers on the blockchain system to implement the action to contain the malware based on the smart contract. In response to the consensus being reached by the plurality of computers, the blockchain system transmits, to the network resource, directions to implement the action on the network resource as specified by the smart contract.

Abstract: A vehicular alert system includes an autonomous aerial vehicle and a central computer. The autonomous aerial vehicle includes a processor, a display, and a detector. The processor controls a data transceiver. The detector detects one or more vehicular condition. The central computer communicates with the autonomous aerial vehicle via the data transceiver. The central computer includes a memory device. The memory device stores vehicular condition data and road condition data. The central computer communicates one of a vehicular condition or a road condition to the autonomous aerial vehicle. The processor of the autonomous aerial vehicle displays the received condition on the display.

Abstract: A processor-implemented method facilitates identity exchange in a decentralized setting. A first system performs a pseudonymous handshake with a second system that has created an identity asset that identifies an entity. The second system has transmitted the identity asset to a third system, which is a set of peer computers that support a blockchain that securely maintains a ledger of the identity asset. The first system transmits a set of pseudonyms to the third system, where the set of pseudonyms comprises a first pseudonym that identifies the first system, a second pseudonym that identifies a user of the second system, and a third pseudonym that identifies the third system. The first system receives the identity asset from the third system, which securely ensures a validity of the identity asset as identified by the first pseudonym, the second pseudonym, and the third pseudonym.

Abstract: A computer-implemented method includes detecting, by one or more sensors of a robot, one or more characteristics of a current point on a surface on which the robot travels. A feature vector is constructed to describe the current point on the surface on which the robot travels, based on the one or more characteristics. The feature vector is mapped to a confidence level that a hazard exists at the current point on the surface. It is determined that the confidence level meets a threshold confidence. An alert is issued in association with the current point on the surface, based on the confidence level meeting the threshold confidence.

Abstract: Anomalous control and data flow paths in a program are determined by machine learning the program's normal control flow paths and data flow paths. A subset of those paths also may be determined to involve sensitive data and/or computation. Learning involves collecting events as the program executes, and associating those event with metadata related to the flows. This information is used to train the system about normal paths versus anomalous paths, and sensitive paths versus non-sensitive paths. Training leads to development of a baseline “provenance” graph, which is evaluated to determine “sensitive” control or data flows in the “normal” operation. This process is enhanced by analyzing log data collected during runtime execution of the program against a policy to assign confidence values to the control and data flows. Using these confidence values, anomalous edges and/or paths with respect to the policy are identified to generate a “program execution” provenance graph associated with the policy.

Abstract: Server resources in a data center are disaggregated into shared server resource pools, which include a pool of secure processors. Advantageously, servers are constructed dynamically, on-demand and based on a tenant's workload requirements, by allocating from these resource pools. According to this disclosure, secure processor modules for new servers are allocated to provide security for data-in-use (and data-at-rest) in a dynamic fashion so that virtual and non-virtual capacity can be adjusted in the disaggregate compute system without any downtime, e.g., based on workload security requirements and data sensitivity characteristics. The approach herein optimizes an overall utilization of an available secure processors resource pool in the disaggregated environment. The resulting disaggregate compute system that is configured according to the approach cryptographically-protects workload data whenever it is outside the CPU chip.

Abstract: Mechanisms are provided for implementing a conversation monitoring system. The conversation monitoring system monitors a conversation between at least two participants and extracting, by the conversation monitoring system, key terms present in communications between the at least two participants. The conversation monitoring system generates a provenance graph data structure based on the extraction of the key terms where the provenance graph data structure has speaker nodes representing the at least two participants, term nodes representing the key terms present in the communications, and edges connecting nodes in the provenance graph. The conversation monitoring system analyzes the provenance graph data structure to identify a relative ranking of the key terms within the conversation and generates an output representing content of the conversation based on the relative ranking of the key terms.

Abstract: Smart speaker system mechanisms, associated with a smart speaker device comprising an audio capture device, are provided for processing audio sample data captured by the audio capture device. The mechanisms receive, from the audio capture device of the smart speaker device, an audio sample captured from a monitored environment. The mechanisms classify a sound in the audio sample data as a type of sound based on performing a joint analysis of a plurality of different characteristics of the sound and matching results of the joint analysis to criteria specified in a plurality of sound models. The mechanisms determine, based on the classification of the sound, whether a responsive action is to be performed based on the classification of the sound. In response to determining that a responsive action is to be performed, the mechanisms initiate performance of the responsive action by the smart speaker system.

Abstract: Mechanisms are provided for implementing a conversation monitoring system. The conversation monitoring system monitors a conversation between at least two participants and extracting, by the conversation monitoring system, key terms present in communications between the at least two participants. The conversation monitoring system generates a provenance graph data structure based on the extraction of the key terms where the provenance graph data structure has speaker nodes representing the at least two participants, term nodes representing the key terms present in the communications, and edges connecting nodes in the provenance graph. The conversation monitoring system analyzes the provenance graph data structure to identify a relative ranking of the key terms within the conversation and generates an output representing content of the conversation based on the relative ranking of the key terms.

Abstract: A method provides a security action based on identity profile scores. One or more processors represent an identity profile as a knowledge graph. The processor(s) associate a set of changes of the identity profile across a plurality of identity networks with a fraud score. The processor(s) then implement a security action based on the fraud score.

Abstract: A method provides a network-agnostic identity broker for retrieving identity records across heterogeneous identity networks. An identity broker receives a client request from a client to retrieve and evaluate user identity information for confirming an identity of a particular entity. The identity broker utilizes a group membership of the client to select a set of policies for handling the client request, and selects an identity network from multiple heterogeneous identity networks as a selected identity network to which the client request is to be sent. The identity broker sends the client request to the selected identity network, and then receives a response from the selected identity network. The identity broker evaluates the response according to the set of policies, such that the evaluated response conforms with the set of policies, and transmits the evaluated response to the client.

Abstract: An example operation may include one or more of creating, by a blockchain user of a blockchain network, a world state checkpoint transaction requesting world state validation, endorsing, by one or more endorser nodes or peers, the world state checkpoint transaction, transferring endorsements to the blockchain user, recording, by an orderer node or peer, the endorsed world state checkpoint transaction into a block, validating and committing all transactions in the block, calculating and signing a hash of a current world state, by all blockchain nodes or peers of the blockchain network, and verifying, by the blockchain user, world state integrity from the calculated and signed hashes of the current world state.

Abstract: A method routes an identity query to a particular identity network. An identity broker determines that a candidate identity network is associated with a confidence score that satisfies predetermined criteria. Responsive to determining that the candidate identity network is associated with the confidence score that satisfies the predetermined criteria, the identity broker onboards the candidate identity network into a set of identity networks services, and then routes an identity query for an identity to the candidate identity network that satisfies the predetermined criteria.

Abstract: A deep-learning based method evaluates similarities of entities in decentralized identity graphs. One or more processors represent a first identity profile as a first identity graph and a second identity profile as a second identity graph. The processor(s) compare the first identity graph to the second identity graph, which are decentralized identity graphs from different identity networks, in order to determine a similarity score between the first identity profile and the second identity profile. The processor(s) then implement a security action based on the similarity score.

Abstract: Smart speaker system mechanisms, associated with a smart speaker device comprising an audio capture device, are provided for processing audio sample data captured by the audio capture device. The mechanisms receive, from the audio capture device of the smart speaker device, an audio sample captured from a monitored environment. The mechanisms classify a sound in the audio sample data as a type of sound based on performing a joint analysis of a plurality of different characteristics of the sound and matching results of the joint analysis to criteria specified in a plurality of sound models. The mechanisms determine, based on the classification of the sound, whether a responsive action is to be performed based on the classification of the sound. In response to determining that a responsive action is to be performed, the mechanisms initiate performance of the responsive action by the smart speaker system.

Abstract: Smart speaker mechanisms are provided for processing audio sample data captured by the audio capture device is provided. The mechanisms capture one or more audio samples from a monitored environment and store the captured one or more audio samples in the audio sample buffer. The one or more captured audio samples are analyzed to determine whether the one or more captured audio samples represent a potential wake sound for initiating a responsive action by the smart speaker device. The potential wake sound is a variable sound determined to not be a normal ambient sound of the monitored environment. In response to determining that the one or more captured audio samples represent a potential wake sound for initiating a responsive action by the smart speaker device, the mechanisms initiate performance of the responsive action by the smart speaker device.