Abstract: An example operation may include one or more of creating a source file, segmenting the source file into source file segments, creating a number of auxiliary data segments corresponding to source file segments, performing a chameleon hash of the source file segments and the auxiliary data segments, obtaining a source file signature from the chameleon hash, performing a cryptographic hash of the auxiliary data segments, obtaining an auxiliary data signature from the cryptographic hash, and storing the source file and cryptographic signatures to a shared ledger of a blockchain network. Each auxiliary data segment includes a random string of data that is generated based on a corresponding source file segment.

Abstract: A system, method and program product for authenticating a device. An authentication service is provided having: a data management system for periodically collecting and storing signature data from each of a set of registered devices, wherein the signature data includes a plurality of data points, and wherein at least one of the data points includes a device usage characteristic; a system for obtaining a temporal signature state (TSS) vector of a device in response to a transaction request from the device, wherein the TSS vector includes values for a selected subset of the data points forming the signature data; and an authenticator for comparing the TSS vector of the device with stored signature data in order to authenticate the device.

Abstract: A system, method and program product for implementing image recognition. A system is disclosed that includes a training system for generating a multi-feature multi-matcher fusion (MMF) predictor for scoring pairs of images, the training system having: a neural network configurable to extract a set of feature spaces at different resolutions based on a training dataset; and an optimizer that processes the training dataset, extracted feature spaces and a set of matcher functions to generate the MMF predictor having a series of weighted feature/matcher components; and a prediction system that utilizes the MMF predictor to generate a prediction score indicative of a match for a pair of images.

Abstract: Mechanisms are provided for training a classifier to identify adversarial input data. A neural network processes original input data representing a plurality of non-adversarial original input data and mean output learning logic determines a mean response for each intermediate layer of the neural network based on results of processing the original input data. The neural network processes adversarial input data and layer-wise comparison logic compares, for each intermediate layer of the neural network, a response generated by the intermediate layer based on processing the adversarial input data, to the mean response associated with the intermediate layer, to thereby generate a distance metric for the intermediate layer. The layer-wise comparison logic generates a vector output based on the distance metrics that is used to train a classifier to identify adversarial input data based on responses generated by intermediate layers of the neural network.

Abstract: Mechanisms are provided to provide an improved computer tool for determining and mitigating the presence of adversarial inputs to an image classification computing model. A machine learning computer model processes input data representing a first image to generate a first classification output. A cohort of second image(s), that are visually similar to the first image, is generated based on a comparison of visual characteristics of the first image to visual characteristics of images in an image repository. A cohort-based machine learning computer model processes the cohort of second image(s) to generate a second classification output and the first classification output is compared to the second classification output to determine if the first image is an adversarial image. In response to the first image being determined to be an adversarial image, a mitigation operation by a mitigation system is initiated.

Abstract: A face clustering system for video face clustering in a video sequence, the system including an inherent supervision summarization device configured to collect group-level supervision and instance level supervision within a same chunklet based on a user input of face images for a person, a discriminative projection learning device configured to embed group constraints of the group-level supervision into a transformed space, and configured to generate an embedding space from the original image feature space, and a clustering device, in the embedding space, configured to execute pair-wise based clustering to cluster the video images into different clusters with the instance level supervision collected by the inherent supervision summarization device.

Abstract: ML model(s) are created and trained using training data from user(s) to create corresponding trained ML model(s). The training data is in FHE domains, each FHE domain corresponding to an individual one of the user(s). The trained machine learning model(s) are run to perform inferencing using other data from at least one of the user(s). The running of the ML model(s) determines results. The other data is in a corresponding FHE domain of the at least one user. Using at least the results, it is determined which of the following issues is true: the results comprise objectionable material, or at least one of the trained ML model(s) performs prohibited release of information. One or more actions are taken to take to address the issue determined to be true. Methods, apparatus, and computer program product are disclosed.

Abstract: A computer-implemented method, data processor and computer program product determine exposure levels to external stimuli. At least one environmental condition is monitored and an external stimulus event is identified based on the at least one environmental condition. An intensity level of the external stimulus event is determined to exceed a predetermined threshold. An exposure level of the external stimulus is determined by integrating the intensity level over time. At least one human biometric quantity of a user is measured and a personal exposure level limitation is determined for the user based on the measured at least one human biometric quantity. When the exposure level exceeds the personal exposure level limitation, the user is warned of the exposure level. The human biometric quantity is one of: heart rate, blood pressure, body temperature, glucose level, blood oxygen level, muscle activity, electrolyte level, and lactic acid level.

Abstract: An example operation may include one or more of joining, by a host device, a blockchain managed by one or more devices on a decentralized network, the blockchain is configured to use one or more smart contracts that specify transactions among a plurality of end-users, creating on the blockchain the smart contract defining authentication parameters for an authentication of an end-user from the plurality of the end-users, executing the smart contract to perform the authentication of the end-user associated with a transaction based on the authentication parameters by generating an authentication challenge for the transaction, and recording an authentication log produced by the authentication challenge into a metadata of a transaction payload for analytics.

Abstract: Mechanisms are provided to implement a privacy enhanced location service for determining a granularity of location information to return to a requestor computing device. The privacy enhanced location service receives, from a requestor computing device, a location query requesting location information for a subject. The privacy enhanced location service retrieves a selected subject privacy policy data structure, selected from a set of subject privacy policy data structures corresponding to the subject identified in the location query. The privacy enhanced location service applies the selected subject privacy policy data structure to location information associated with the subject to generate modified location information having a granularity of location information specified in the selected subject privacy policy data structure. The privacy enhanced location service transmits the modified location information to the requestor computing device.

Abstract: An embodiment of the invention provides a method for secure biometrics matching with split phase client-server matching protocol, wherein a first biometric input is received in an electronic device. The first biometric input is stored in the electronic device as a biometric profile; and, the biometric profile is sent to a server. An additional biometric input is received from a user in the electronic device; and, the additional biometric input is compared to the biometric profile stored in the electronic device to generate a local matching score. The additional biometric input is sent to the server. The local matching score and a remote matching score generated by the at least one server are compared; and, it is determined whether to authenticate the user based on the comparison of the local matching score and the remote matching score.

Abstract: A system, method and program product for implementing image recognition. A system is disclosed that includes a training system for generating a multi-feature multi-matcher fusion (MMF) predictor for scoring pairs of images, the training system having: a neural network configurable to extract a set of feature spaces at different resolutions based on a training dataset; and an optimizer that processes the training dataset, extracted feature spaces and a set of matcher functions to generate the MMF predictor having a series of weighted feature/matcher components; and a prediction system that utilizes the MMF predictor to generate a prediction score indicative of a match for a pair of images.

Abstract: An example operation may include one or more of detecting a suspected biometric authentication incident, submitting a first blockchain transaction including a first report to a blockchain network, submitting a second blockchain transaction including a second report to the blockchain network, and taking an action, by one or more blockchain nodes, in response to determining one or more of the first and second reports are relevant to the one or more blockchain nodes. The first report includes public and private data corresponding to the suspected biometric authentication incident, and the second report includes one or more of a root cause and mitigation steps for the incident.

Abstract: Embodiments include method, systems and computer program products for providing medication-related feedback. Aspects include receiving medication information for a patient. Aspects also include receiving a biological, behavioral, or environmental output from a sensor. Aspects also include determining, based upon the biological, behavioral, or environmental output and the medication information for the patient, whether a medication dose is needed. Aspects also include, based on a determination that the medication dose is needed, generating an alert.

Abstract: Parallel linguistic corpora used to train cognitive translation systems are built from robot plans. A collection of robot plans in various languages is analyzed in candidate pairings to see if any two plans are directed to the same task. If so, verbal instructions in different languages from the two plans (such as plan names) are assumed to have the same meaning, and are stored in association as part of the bilingual corpus. If each plan has multiple steps, the corresponding steps from the two plans can also be stored in association as part of the corpus. Robot plans are modeled as finite state automata having a plurality of links representing sensory states and a plurality of nodes representing motor actions, and the determination of plan similarity is based on a comparison of the two FSAs.

Abstract: An example operation may include one or more of determining, by a file redaction device, redacted segments of a source file, receiving, by a signature update device, the redacted source file segments, a stored trapdoor key, and stored auxiliary data segments, determining modified auxiliary data from the redacted source file segments, the trapdoor key and the auxiliary data segments, executing chaincode to obtain a modified auxiliary data signature and identifiers of the redacted source file segments, and storing the modified auxiliary data signature and identifiers of the redacted source file segments to a shared ledger of a blockchain network. Each stored auxiliary data segment including a random string of data corresponding to a segment of the source file.

Abstract: An example operation may include one or more of creating a source file, segmenting the source file into source file segments, creating a number of auxiliary data segments corresponding to source file segments, performing a chameleon hash of the source file segments and the auxiliary data segments, obtaining a source file signature from the chameleon hash, performing a cryptographic hash of the auxiliary data segments, obtaining an auxiliary data signature from the cryptographic hash, and storing the source file and cryptographic signatures to a shared ledger of a blockchain network. Each auxiliary data segment includes a random string of data that is generated based on a corresponding source file segment.

Abstract: An example operation may include one or more of initiating, by a file verification device, verification of a source file or a redacted source file, executing one of a smart contract or chaincode to verify the chameleon hash signature and the auxiliary data hash signature, and providing a notification whether the verification was successful or unsuccessful. In response to initiating verification of the source file, the method further includes the file verification device receiving stored source file segments and stored auxiliary data segments, generating a chameleon hash signature, and generating an auxiliary data hash signature. In response to initiating verification of the redacted source file, the method further includes receiving stored redacted file segments, stored auxiliary data segments, and stored modified auxiliary data, generating a chameleon hash signature, and generating an auxiliary data hash signature.

Abstract: A method establishes a session between a network service and a user device used by the user having a particular sociometric identity. One or more processors identify an interaction between a user and one or more provider entities. The processor(s) identify profiles for the one or more provider entities, where each profile of the one or more provider entities describes a type of service provided by a respective provider entity of the one or more provider entities, and compute a sociometric identity of the user based on the profiles of the one or more provider entities with which the user has had an interaction. One or more processors transmit the sociometric identity to a network service in order to establish a session between the network service and a user device used by the user having the sociometric identity.

Abstract: An anonymized biometric representation of a target individual is used in a computer based security system. A detailed input biometric signal associated with a target individual is obtained. A weakened biometric representation of the detailed biometric signal is constructed such that the weakened biometric representation is designed to identify a plurality of individuals including the target individual. The target individual is enrolled in a data store associated with the computer based security system wherein the weakened biometric representation is included in a record for the target individual. In another aspect of the invention, a detailed input biometric signal from a screening candidate individual is obtained. The detailed biometric signal of the screening candidate is matched against the weakened biometric representation included in the record for the target individual.