Abstract: Methods and systems for determining and reacting to trust relationships in a messaging group are described herein. A computing device may detect text associated with a messaging group comprising a plurality of different users. The computing device may process the text to determine at least one topic, then generate a trust graph comprising a plurality of connections between a user and each of the plurality of different users. Each of the plurality of connections may indicate a degree of trust corresponding to the at least one topic. The computing device may determine, based on the trust graph, that a first degree of trust, associated with at least one of the plurality of different users, satisfies a threshold. The computing device may cause output of a notification comprising an identity of the at least one of the plurality of different users.

Abstract: A computing system can execute a continuous integration (CI) pipeline for merging a code commit into a software project. The CI pipeline can include CI phases that can output sets of result data. Each CI phase can generate a respective set of result data among the sets of result data. The computing system can determine delay times associated with the sets of result data, each respective set of result data being assigned a corresponding delay time among the delay times. For each respective delay time, the computing system can prevent a set of users from accessing the respective set of result data prior to an expiration of the corresponding delay time. The computing system can also permit the set of users to access the respective set of result data following the expiration of the corresponding delay time.

Abstract: An encryption processing apparatus processes a ciphertext. The ciphertext is a fully homomorphic ciphertext that has, as a plaintext associated with an integer, a value obtained by adding an error with a predetermined variance to a predetermined value and that is able to be subjected to a predetermined operation between integers without being decrypted. The apparatus includes a processor that performs a process including a calculation process of performing a scalar remainder operation on a ciphertext of a dividend based on a first polynomial obtained from a plaintext of a divisor to obtain a new ciphertext corresponding to a remainder.

Abstract: A method for user credential authentication includes receiving real-time logs from a blockchain network that is configured to perform an authentication process of a user. The real-time logs are normalized and bucketized to generate processed real-time logs, which are stored in a block and are added to a blockchain. The processed real-time logs are analyzed to identify the plurality of real-time behavioral patterns of the user. A first authentication score is determined by comparing a first real-time behavioral pattern to a respective first historical behavioral pattern. The first authentication score is compared to a first authentication score threshold, which corresponds to the first real-time behavioral pattern and the respective first historical behavioral pattern. In response to the first authentication score being less than the first authentication score threshold, a first instruction is sent to the blockchain network to temporarily stop the authentication process of the user.

Abstract: Data access requests are grouped into a plurality of clusters according to an activity count within each of a plurality of time periods. Based on a time separating two clusters in the plurality of clusters, a time window in which a data access policy applies is defined. Using the plurality of data access requests, a forecasting model is trained to predict a volume of future data access requests, the training resulting in a trained forecasting model. A data access policy effective during the time window and conditioned on a threshold related to the volume of future data access requests is generated. Responsive to determining that a new data request meets a criterion of the data access policy, processing of the new data request is allowed.

Abstract: Disclosed herein are systems, methods, and processes for a machine learned alert triaging classification (ATC) system that uses machine learning techniques to generate an alert triage classification model that can be trained and deployed in modern security operation centers to optimize alert triaging and cyber threat classification. A training dataset of classified records is obtained. Each classified record in the training dataset includes detection characteristics data of a set of machines and threat classification results produced by performing alert triage classification of detection messages associated with the set of machines. An ATC model is trained using the training dataset according to a machine learning technique. The training tunes the ATC model to classify, based on at least the detection characteristics data, a new detection message associated with a machine from the set of machines as a threat or as not a threat.

Abstract: The present disclosure provides systems and methods for substantially continuous and dynamic vulnerability scoring. According to the present disclosure, the method includes detecting one or more vulnerabilities. The method includes determining a contextual prioritization score (CPS) for each of the one or more vulnerabilities based on historical data, the historical data including a series of contextual features corresponding to each one of the one or more vulnerabilities. The method may include, in response to detection of an event, determining a partial CPS score by an agent. The method may include, if a new partial CPS is determined, generating an updated CPS based on the CPS and the new partial CPS and transmitting the updated CPS to each of one or more computing devices.

Abstract: Techniques are described herein for running multiple logical secure elements (LSEs) on the same physical secure element (SE) hardware. For example, embodiments may include running multiple logical Subscriber Identification Modules (SIM) cards on the same physical SIM card or universal integrated circuit card (UICC). Additionally or alternatively, embodiments may include running other secure element applications and services on the same SE hardware. The techniques allow for mobile devices users to access multiple security services, which may originate from different security service providers (SSPs), in a secure manner using the same SE hardware without requiring the integration of multiple physical slots on a mobile device or the physical exchange of different cards within the same slot.

Abstract: A method of securely accessing a database with sensitive data, such as the clinical information of patients, by a client in a privacy-preserving manner, including: communicating with the server to obtain tags for specific attribute-value pairs when the client is authorized to make a query; imposing a tag quota per client and restricting tag generation to authorized query terms with valid digital signatures from a third-party authority; storing the tags and their associated query terms in confidence for future queries; sending a combination of tags that define the terms of a conjunctive query over a secure channel to a proxy; receiving from the proxy encrypted coefficients of a polynomial whose roots are indices to the query results; decrypting the encrypted coefficients in a first protocol with the server; calculating the roots of the polynomial based upon the decrypted coefficients and discarding any superfluous roots; obtaining the encrypted records associated with the calculated roots from the proxy; and d

Abstract: A computer-implemented method and a distributed computer system for privacy-preserving distributed training of a global neural network model on distributed datasets. The system has data providers communicatively coupled with each having a respective local training dataset and a vector of output labels for training the global model. Further, it has a cryptographic distributed secret key and a corresponding collective cryptographic public key of a multiparty fully homomorphic encryption scheme, with the weights of the global model being encrypted with the collective public key. Each data provider computes and aggregates, for each layer of the global model, encrypted local gradients using the respective local training dataset and output labels, with forward pass and backpropagation using stochastic gradient descent.

Abstract: An example operation includes one or more of obtaining, by a server, a certificate from an IoT device associated with a vehicle, determining, by the server, the certificate is not a revoked certificate, assigning, by the server, a temporary certificate to the IoT device, and validating, by the server, the temporary certificate.

Abstract: Aspects of the disclosure relate to using machine learning to modify account privacy settings. A computing platform may identify initial account settings for an individual of a plurality of individuals. Using a settings optimization model, the computing platform may identify account data and third party data for the individual. Using the settings optimization model, the computing platform may identify discrepancies between the initial account settings for the first individual and account settings for a subset of the plurality of individuals having common characteristics with the individual. Based on the discrepancies, the computing platform may identify settings modifications for the individual, and may determine that a modification of the settings modifications applies to a detected interaction of the individual. The computing platform may direct an enterprise data source to perform the modification, which may cause the enterprise data source to modify the initial account settings based on the modification.

Abstract: A data analytics system is disclosed that can include a data repository configured to store data for multiple clients, a metadata repository separate from the data store, an access control system, and a policy store. The data analytics system can automatically generate metadata for data in the data repository using a metadata engine, the metadata including technical metadata and usage metadata, and store the metadata in the metadata repository. The data analytics system can obtain a client policy governing access to the data. The data analytics system can receive a request to provide the data, the request including instructions to create a pipeline to provide the data. The data analytics system can authorize, by the access control system, the request using the policy and usage metadata; create the pipeline using the technical metadata; and provide the data using the pipeline.

Abstract: Techniques provided herein employ a device monitoring service to provide task to content consumption devices that are steaming digital media from the streaming infrastructure. The tasks, when implemented by the content consumption devices, may reveal differences in task performance between authorized devices, which have the authorization to access the digital media, and imposter devices, which exploit vulnerabilities in the streaming pipeline to access the digital media. In addition, the techniques provided herein may include a machine learning/artificial intelligence model that is trained to recognize authorized and imposter content consumption devices based on their task performance.

Abstract: The present disclosure provides systems, methods, and computer-readable media for implementing security polices at software call stack level. In one example, a method includes generating a call stack classification scheme for an application, detecting a call stack during deployment of the application; using the call stack classification scheme during runtime of the application, classifying the detected call stack as one of an authorized call stack or an unauthorized call stack to yield a classification; and applying a security policy based on the classification.

Abstract: The present techniques may provide improved processing and functionality of performance of the 128-bit AES Algorithm, which may provide improved power consumption. For example, in an embodiment, an encryption and decryption apparatus may comprise memory storing a current state matrix of an encryption or decryption process and a plurality of multiplexers configured to receive from the memory current elements of the state matrix stored in the memory, perform a cyclic shift on the received elements of the state matrix, and transmit the shifted elements to the memory for storage as a new state matrix.

Abstract: Secure data analytics is provided via a process that identifies sensitive data fields of an initial dataset and mappings between the sensitive data fields and other data fields of the dataset, where analytics processing is to be performed on the initial dataset, then, based on an expectation of data fields, of the initial data set, to be used in performance of the analytics processing and on the identified sensitive data fields, selects and applies a masking method to the initial dataset to mask the sensitive data fields and produce a masked dataset, provides the masked dataset to an analytics provider with a request for the analytics processing, and receives, in response, a generated analytics function, generated based on the masked dataset, that is configured to perform the analytics processing, and invokes the generated analytics function against the initial dataset to perform the analytics processing on the initial dataset.

Abstract: Aspects of the disclosure relate to information masking. A user device may receive a request to access information that includes personal identifiable information (PII) and retrieve source data comprising the PII. The user device may mask, within the source data and based on a data management policy, the PII, resulting in masked information. The user device may display the masked information. The user device may receive a request to unmask the masked information and unmask the PII, resulting in unmasked PII. The user device may display the unmasked PII and send unmasking event information to a PII footprint modeling platform, which may cause the PIT footprint modeling platform to: log the request to unmask the masked information in an unmasking event log, 2) apply a machine learning model to the unmasking event log to identify malicious events, and 3) trigger remediation actions based on identification of the malicious events.

Abstract: A processor can be configured to receive data associated with, and/or access to, a computing system's file system structure. The processor can also be configured to determine file patterns, file path patterns and/or graph patterns associated with the computing system. The processor can also be configured to build a graph structure having nodes and edges, the graph structure representing the file patterns, file path patterns and graph patterns, wherein the nodes of the graph structure represent files and attributes of the files and the edges of the graph structure represent connectivity between the files. The processor can also be configured to train, based on the graph structure, a first machine learning model to learn a feature vector associated with a file. The processor can also be configured to train, based on the feature vector, a second machine learning model to identify a vulnerable ransomware target.

Abstract: An aspect of the present disclosure relates to one or more data decryption techniques. In embodiments, an input/output operation (IO) stream including one or more encrypted IOs is received by a storage array. Each encrypted IO is assigned an encryption classification. Further, each encrypted IO is processed based on its assigned encryption classification.