International Business Machines Corporation

Killian Levacher

Stefano Braghin

Marco Simioni

Mathieu Sinn

Emanuele Ragnoli

Martin Stephenson

Ngoc Minh Tran

Ambrish Rawat

Beat Buesser

Rahul Nair

Lea Deleris

Naoise Holohan

Gokhan Sagirlar

Elizabeth Daly

Yassine Lassoued

Yufang Hou

Spyridon Antonatos

Charles Arthur Jochim

Debasis Ganguly

Christian Pinto

Stephane Deparis

Francesca Bonin

Mark Purcell

Hessel Tuinhof

Cennet Oguz

Giulio Zizzo

A method, computer program, and computer system are provided for predicting and assessing risks on websites. Data corresponding to historical interactions of a user with one or more websites is accessed. A simulation of actions of the user is generated based on the accessed data, and actions of the user are simulated on a pre-defined target website based on the generated simulation of the actions of the user. Risks on the target website are identified based on simulating the actions of the user. The website is updated to mitigate the identified risks.

Federated generative models for website assessment

Disclosed are techniques for linking information about individual entities across multiple datasets. A target dataset with some information corresponding to at least one attribute of an entity is received. Semantic processing is performed on the target dataset to extract semantic representations of the information and corresponding attributes, which is utilized to search at least one other dataset for additional information that is absent from the target dataset, corresponding to at least one attribute of the entity, which are used to augment the target dataset with additional information corresponding to the entity. This is repeated iteratively, with each subsequent iteration including semantic representations of information found in the searches of previous iterations until no additional information about the entity is found when searching the multiple datasets with semantic representations of the now augmented target dataset. In some embodiments the augmented target dataset is used for determining privacy risks for the entity.

Augmented privacy datasets using semantic based data linking

One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to facilitating a process to compensate a service being provided over a network connection. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise a determination component that determines a network connection between a server and a client node, and a predictive component that predicts, employing machine learning, a graphical representation update to a service provided by the server over the network connection. The predictive component can generate the prediction in response to a decrease in bandwidth and/or an increase in latency of a network connection. A training component can train a machine learning model employed by the predictive component based on historical data of the service provided by the server.

AI-based compensation of resource constrained communication

Various embodiments are provided for automated evaluation of machine learning models in a computing environment by one or more processors in a computing system. A level of robustness of a machine learning model against adversarial whitebox operations may be evaluated and determined by applying a data set used for testing the machine learning model, one or more adversarial operation objectives, an adversarial threat model, and a selected number of hyperparameters. Results from the adversarial operation may be analyzed and a modified machine learning model may be generated while performing the evaluating and determining.

Automated evaluation of machine learning models

Adversarial attack detection operations may be applied on one or more deep generative models for defending deep generative models from adversarial attacks. The adversarial attack may be detected on the one or more deep generative models based on the one or more of a plurality of adversarial attack detection operations. The one or more deep generative models may be sanitized based on the adversarial attack.

Defending deep generative models against adversarial attacks

Embodiments for ensemble training in a distributed marketplace in a computing environment. One or more ensemble machine learning models may be provided from a plurality of machine learning models competing within the distributed marketplace that achieve a performance on ensemble training data equal to or greater than a selected performance threshold, wherein the distributed marketplace is a blockchain.

Ensemble training in a distributed marketplace

Embodiments of the invention are directed to techniques that include receiving a query intended for a targeted database and determining that the query is from an unauthorized user. A response is returned to the unauthorized user generated by a model, the response being dynamically generated to fulfill the query. The model is configured to generate responses consistent with any previous responses returned to the unauthorized user.

Defense of targeted database attacks through dynamic honeypot database response generation

Systems, computer-implemented methods, and computer program products that can facilitate sensitive data policy recommendation are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an extraction component that can employ an artificial intelligence model to extract compliance data from a data source. The computer executable components can further comprise a recommendation component that can recommend a sensitive data policy based on the compliance data. In some embodiments, the recommendation component can further identify one or more sensitive data entities of a sensitive data dataset that are affected by actionable obligation data of the data source.

Sensitive data policy recommendation based on compliance obligations of a data source

A method, computer system, and a computer program product for text data protection is provided. The present invention may include receiving a text data. The present invention may also include identifying a portion of the received text data having a highest impact on a first confidence score associated with a target model prediction. The present invention may further include generating at least one semantically equivalent text relative to the identified portion of the received text data. The present invention may also include determining that the generated at least one semantically equivalent text produces a second confidence score associated with the target model prediction that is less than the first confidence score associated with the target model prediction. The present invention may further include generating a prompt to suggest modifying the identified portion of the received text data using the generated at least one semantically equivalent text.

Text data protection against automated analysis

Various embodiments are provided for managing regulatory compliance for an entity in a computing environment by a processor. A law, policy, regulation, or a combination thereof extracted from one or more segments of text data from one or more data sources may be identified requiring an obligation to be performed by the entity.

Managing regulatory compliance for an entity

Embodiments include a method for one or more processors to receive an organic dataset and a domain knowledge base. The one or more processors identify private data entities present within the organic dataset. The one or more processors determine statistical properties of the private data entities identified within the organic dataset. The one or more processors create a plurality of test data templates by removing the private data entities from the organic dataset. The one or more processors select from the domain knowledge base, synthetic data entities that match a data type of the removed private data entities, respectively, and align with the statistical properties of the private data entities, and the one or more processors generate synthetic test data by inserting, respectively, the synthetic data entities of the matching data type for the removed private data entities in the test data templates.

Synthetic deidentified test data

Methods, systems and computer program products for protection of content repositories using dynamic watermarking are provided. Aspects include receiving a request for a code stored in a content repository from a user and identifying a plurality of candidate locations in the code to insert watermarks. Aspects also include generating one or more watermarks and inserting the one or more watermarks in a subset of the plurality of candidate locations in the code. Aspects further include providing the code, including the one or more watermarks, to the user.

Protection of a content repository using dynamic watermarking

A system and method for implementing a marketplace for risk assessed smart contracts issuers and execution providers in blockchains in a computer environment are presented. A smart contract, issued by one or more blockchain nodes to a secondary blockchain and accepted by a primary blockchain, may be executed following a risk assessment to recursive call attack vulnerabilities of the smart contract according to a reputation of both the one or more blockchain nodes and the one or more execution nodes.

Implementing a marketplace for risk assessed smart contracts issuers and execution providers in a blockchain

Embodiments are provided for intelligent augmentation of word representations in a computing system by a processor. One or more character shape embeddings may be created in an artificial neural network (ANN) by augmenting one or more word representations based on a set of natural language processing (NLP) data, a set of word embedding vectors, and one or more clustering operations. A shape, a category, and a contextual identify of the one or more word representations may be analyzed used in creating the one or more character shape embeddings.

Intelligent augmentation of word representation via character shape embeddings in a neural network

A system and method for reducing risk of smart contracts in blockchains in a computer environment are presented. One or more smart contracts may be accepted or rejected from a secondary blockchain to a primary blockchain according to a risk assessment to recursive call attack vulnerabilities.

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Dublin, Ireland

Killian Levacher