International Business Machines Corporation

Pankaj Satyanarayan Dayama

Srikanth Govindaraj Tamilselvam

Priyanka Agrawal

Amrita Saha

Nitin Singh

Vinayaka D Pandit

Jignesh K Karia

Mukundan Sundararajan

Krishnasuri Narayanam

Akshar Kaul

Neha Shah

Vishal Awal

Dhinakaran Vinayagamurthy

Vijay Ekambaram

Shivkumar Kalyanaraman

Shilpa Shetty

Rishi Saket

Ken Kumar

Santosh Ravi Kiran Penubothula

Pratyush Kumar

Yedendra Shrinivasan

Vikas Raykar

Vikas Chandan

Samarth Bharadwaj

Sukanya Randhawa

Jitan S Chandanani

Prabuchandran Krithivasan Jayachandran

Lohit K Namboodiri

Santosh Rk Penubothula

Suman Das

Sarbajit Kumar Rakshit

Nitin Kumar Gupta

Amit Anil Nanavati

Malolan Chetlur

Padmanabha Venkatagiri Seshadri

Amith Singhee

Saurabh Gupta

Jagabondhu Hazra

Jonathan Bnayahu

Artem Barger

Manikandan Padmanaban

Saneem Ahmed Chemmengath

Kedar Kulkarni

Abhishek Singh

Ramachandra Kota

Eliezer Segev Wasserkrug

Satyam Dwivedi

Ayushi Dalmia

Kalyan Dasgupta

Arindam Dasgupta

Roy Abitbol

Sudip Dutta

Sandeep Nishad

Sai Koti Reddy Danda

Deepa Dubey

Santosh R K Penubothula

Kameshwaran Sampath

Harish Guruprasad Ramaswamy

Varun R Embar

Sarika Chandramohan

Mark A Weitner

Harsh Chaudhari

Johannes Adrianus Wilhelmus Röling

Joseph Nicolaas Gijsbertus Zeinstra

Pankaj Verma

A condition resolution system is provided for facilitating resolution of a condition for an entity. The system obtains an indication of a condition to be resolved for the entity, groups the entity with one or more other entities into a group of entities, and ascertains one or more options for resolving the condition using the condition to be resolved and option-related data associated with entities of the group of entities. The condition resolution system further evaluates the one or more options to select an option to resolve the condition for the entity, and determines a risk score for the selected option for resolving the condition. The risk score is based on one or more risk factors, and based on the risk score being acceptable, the system initiates an action based on the selected option to resolve the condition for the entity.

Condition resolution system

A system and method for deactivating telephone numbers are presented. The method includes, in response to a request to deactivate a telephone number, communicating a query to a blockchain for the telephone number, wherein a block on the blockchain identifies an organization that had sent a previous message to the telephone number. The method also includes determining, based on a response to the query, the organization and generating and activating a filter configured to stop a future message sent by the organization to the telephone number.

Telephone number deactivation process

In an approach, a processor receives a query relating to mobile number porting on a mobile network that includes a hierarchy including a mobile network operator and a mobile virtual network operator, the query requiring access to customer private data. A processor identifies a minimal number of participants in the mobile network with access to the customer private data. A customer provides a response to the query.

Securely responding to queries involving mobile virtual network operators on a mobile network

A method, computer system, and a computer program product for message directing are provided. A first computer receives a registration of members organized into a first closed group hierarchy. The first computer receives a notification of a sensed change of an element in an environment of the first closed group hierarchy. The first computer receives a first message from an external device. The first message relates to the changed element. The first computer selects a first member of the first closed group hierarchy for receipt of the first message. The selecting is based on message routing guidelines established for the first closed group hierarchy. In response to receiving the notification of the sensed change, the first computer transmits the first message to the selected first member of the first closed group hierarchy.

Routing preference based communication to a closed group hierarchy

Promotional offers are offered to telecom subscribers in roaming network areas by recording promotional preferences of subscribers for receiving promotional calls, forwarding subscriber information from a local network telecom service provider to a roaming network service provider, and measuring with the roaming network telecom service provider the subscribers that are present within a roaming network geography. The subscribers can then be ranked by their duration within the roaming network geography, and promotional pools can be generated for the subscribers within the roaming network based on their preferences. Promotional offers can then be matched to the subscribers in the promotional pools based using the ranking of subscribers and the promotional preferences.

Matching promotions to telecom user preferences using artificial intelligence

Message delivery in cellular roaming scenarios involves a user device activated with a home telecommunications service provider (TSP) that provides cellular service to the user device. The user device is located in a remote location and the user device is activated with a remote TSP providing roaming cellular service to the user device in a remote location on a cellular network of the remote TSP. A process includes, based on the user initiating a transaction with a remote application server that requires user authentication based on delivery of a transactional text message, receiving from the remote application server the transactional text message, encrypting the transactional text message to produce an encrypted transactional text message, and forwarding the encrypted transactional text message to the remote TSP for delivery as a short message service (SMS) text to the user device in the remote location via the cellular network of the remote TSP.

Message delivery in cellular roaming scenarios

Methods, apparatus, and processor-readable storage media for facilitating a secure platform for point-to-point brain sensing are provided herein. An exemplary method includes presenting a user of a brain-computer interface with learning content; monitoring brain signals of the user using a brain-computer interface while the learning content is being presented; determining, based at least in part on said monitoring, that a confusion state of the user exceeds a personalized confusion threshold in regard to at least a part of the learning content; in response to said determining, outputting information to assist the user in understanding the part of the learning content until the confusion state of the user is below the personalized confusion threshold.

Secure platform for point-to-point brain sensing

An example operation may include one or more of querying, via an application programming interface (API), a blockchain ledger for attributes of a shipment by a carrier from an origin location to a destination location, predicting, via an artificial intelligence (AI) model, one or more future events that will occur during the shipment based on the attributes of the shipment retrieved from querying the blockchain ledger, generating, via a smart contract, an accelerated e-invoice based on the one or more future events predicted by the AI model, and storing the accelerated e-invoice on the blockchain ledger.

Accelerated invoicing using predictive freight events

A processor may commit, anonymously, an identity and associated data of a resource producer on a blockchain by an independent gateway. The processor may generate a Merkle tree hash commitment to the blockchain of all resources handled by an aggregator. The processor may execute individual commitments of a resource to a user. The processor may provide a zero-knowledge-proof that proves that the commitment of the identity and associated data of the resource producer and the Merkle tree hash commitment match.

Trusted aggregation with data privacy based on zero-knowledge-proofs

Promotional offers are presented to users by tagging users of the services with the user preferences. Promotional offers are received from vendors. The vendors are classified as being in a home area or roaming areas for the services. The users are tracked through movement of a device for the user that is receiving the services to determine whether the user is in the home area or the roaming areas. Promotional offers from the vendors are matched to the user preferences according to the tagging of the users, wherein the promotional offers are from the home area of the home network or the roaming area of the roaming network.

Matching regional promotions to telecom user preferences using artificial intelligence

Aspects of the present invention disclose a method for calculating a risk resulting from a network of networks that includes unknown relationships in a privacy preserving manner. The method includes one or more processors determining a set of conditions corresponding to a user of a network. The method further includes transmitting a compliance request corresponding to the set of conditions to one or more members of the network utilizing a privacy preserving algorithm. The method further includes determining a respective risk factor of one or more members of the network, wherein the respective risk factor corresponds to a response of each of the one or more members to the compliance request. The method further includes determining an overall risk of the network based at least in part on the risk factors of the one or more members.

Mitigating risk in business networks in a privacy preserving manner

One embodiment provides a method, including: receiving, from a user at a collaboration platform, a request to perform a computation; generating a workflow comprising a sequence of steps for performing the computation; identifying potential data sources comprising the type of data and able to assist in performing at least one of the sequence of steps of the workflow; selecting computation data sources that collaborate to perform the computation, wherein the selecting is performed dynamically and based upon characteristics of a network created by the collaboration platform and between the computation data sources; and facilitating performance of the computation by the computation data sources using data of the computation data sources, wherein during performance of the computation the computation data sources collaborate within the network to perform the workflow while maintaining individual privacy of the data of the computation data sources and providing proof verifying a trustworthiness of the computation.

Platform for dynamic collaborative computation with confidentiality and verifiability

Methods, systems, and computer program products for a secure database-as-a-service are provided herein. A computer-implemented method includes obtaining, by a database service, data associated with one or more client devices to be stored in at least one encrypted database; encrypting, without mutation and in accordance with one or more security requirements, at least a portion of the data using an order preserving encryption scheme, wherein the encrypting comprises (i) computing a set of encryption parameters for the portion of the data and (ii) applying a process that converts a fixed-length cipher text corresponding to the portion of the data to a variable-length cipher text; and storing the encrypted data in the at least one encrypted database, wherein the database service enables one or more indexes to be built over the encrypted data to improve performance of query processing.

Secure database-as-a-service system

One embodiment provides a method, including: receiving a query from a user; providing the query to data owners, wherein each of the data owners has a local machine learning model and wherein the plurality of data owners train a meta-model; secret sharing model output from the data owners between the other data owners, wherein the model output comprises an output responsive to the query computed using the local machine learning model; receiving, from each of the plurality of data owners, a set of meta-features corresponding to the query; and generating a response to the query, wherein the generating comprises determining, by evaluating the meta-model using the set of meta-features received from each of the plurality of data owners, weights for outputs from the local machine learning models and aggregating the outputs in view of the weights.

Secure ensemble training and inference using heterogeneous private machine learning models

A method for deduplicating a distributed ledger, a blockchain, and a computer program product. One embodiment may comprise splitting a new block on a distributed ledger into a plurality of partitions, comparing the plurality of partitions to a deduplication (dedup) map, replacing a first partition of the plurality of partitions with a first reference to the dedup map to produce a transformed block, and communicating the transformed block via a network interface to one or more peers.

Data deduplication in blockchain platforms

Growing community of inventors

Bengaluru, India

Pankaj Satyanarayan Dayama