International Business Machines Corporation

Airbnb, Inc.

Roman Vaculin

Kuntal Dey

Seema Nagar

Karan Ahuja

Karthikeyan Shanmugam

Rong Liu

Ashish Jagmohan

Gennaro A Cuomo

Donna N Dillenberger

James Thomas Rayfield

Sudhanshu Shekhar Singh

Kush Raj Varshney

Elham Khabiri

Richard B Segal

Sharathchandra UmapathiRao Pankanti

Nalini Kanta Ratha

Nianjun Zhou

Karthik Nandakumar

Gi-Joon Nam

Maja Vukovic

Daniel Joseph Friedman

Chandrasekhar Narayanaswami

Michele M Franceschini

Yu Deng

Hong Min

Richard Harold Boivie

Sheng Hua Bao

Ying Li

Jim Alain Laredo

Ehud Aharoni

Omri Soceanu

Sharathchandra Umapathirao Pankanti

Kanthi K Sarpatwar

Venkata Sitaramagiridharganesh Ganapavarapu

Jinfeng Yi

Wei Sun

Michael James Hind

Sekou Lionel Remy

Nelson Kibichii Bore

Utkarsh Dwivedi

Ravi Kiran Raman

Eleftheria K Pissadaki

Heena Bansal

Fenno F Heath, Iii

Stacy Fay Hobson

Matous Havlena

Kanthi Sarpatwar

David T Adamo, Jr

Qi Lei

Frederick Y Wu

Qi Hu

Rema Ananthanarayanan

Jun-Jang Jeng

Enriquillo Valdez

Arun Paidimarri

Lam Nguyen

Pavankumar Murali

Anil Nigam

Jinwook Jung

Seep Goel

Saket Sathe

Abhilash Narendra

Stephane Michel

Nigel Hinds

Yuan Yuan Jia

Hong Lin Guo

Surya Shravan Kumar Sajja

Zhiyuan Li

Pooja Aggarwall

Chian-Rou Shieh

Zhe Shan

A computer implemented method using an artificial intelligence (A.I.) module to explain large scale scheduling solutions includes receiving an original instance of a resource constrained scheduling problem. The instance includes a set of tasks and a variety of resource requirements and a variety of constraints. An optimizer process determines a schedule for the set of tasks while minimizing a makespan of the schedule. A minimal set of resource links is generated based on resource dependencies between tasks. The resource links are added to the original instance of scheduling problem, as precedence constraints. All the resource constraints are removed from the original instance of the resource constrained scheduling problem. A set of critical tasks is computed using a non-resource constrained critical path. Schedules are provided with an explanation of an optimized order of the set of tasks based on the use of the non-resource constrained critical path.

Optimizer agnostic explanation system for large scale schedules

A method, computer system, and a computer program product for managing digital assets is provided. The present invention may include receiving, in a second blockchain network, an authentication request to determine an authenticity of a new digital asset. The present invention may include determining, in the second blockchain network, the authenticity of the new digital asset. The present invention may include updating, in the second blockchain network, the authenticity of the new digital asset as authentic in a shared library. The present invention may include generating, in the second blockchain network, an update authenticity transaction, wherein the update authenticity transaction is sent to a first blockchain network.

Tracing and verifying a shared library using blockchain

A method, apparatus and computer program product that provides multi-phase privacy-preserving inferencing in a high throughput data environment, e.g., to facilitate fraud prediction, detection and prevention. In one embodiment, two (2) machine learning models are used, a first model that is trained in the clear on first transaction data, and a second model that is trained in the clear but on the first transaction data, and user data. The first model is used to perform inferencing in the clear on the high throughput received data. In this manner, the first model provides a first level evaluation of whether a particular transaction might be fraudulent. If a transaction is flagged in this first phase, a second more secure inference is then carried out using the second model. The inferencing performed by the second model is done on homomorphically encrypted data. Thus, only those transactions marked by the first model are passed to the second model for secure evaluation.

Multi-phase privacy-preserving inferencing in a high volume data environment

In an approach for training machine-learning models using encrypted data, a processor receives a set of encrypted data from a client computing device. A processor trains a machine-learning model using a boosting algorithm. A processor performs a first classification on the set of encrypted data using the machine-learning model. A processor sends a first set of encrypted results of the first classification to the client computing device. A processor receives a first set of boosting updates from the client computing device. A processor applies the first set of boosting updates to the machine-learning model.

Enabling boosting protocols on encrypted data

Techniques that facilitate machine learning using multi-dimensional time series data are provided. In one example, a system includes a snapshot component and a machine learning component. The snapshot component generates a first sequence of multi-dimensional time series data and a second sequence of multi-dimensional time series data from multi-dimensional time series data associated with at least two different data types generated by a data system over a consecutive period of time. The machine learning component that analyzes the first sequence of multi-dimensional time series data and the second sequence of multi-dimensional time series data using a convolutional neural network system to predict an event associated with the multi-dimensional time series data.

Multi-dimensional time series event prediction via convolutional neural network(s)

Privacy-preserving homomorphic inferencing utilizes batch processing on encrypted data records. Each data record has a private data portion of interest against which the inferencing is carried out. Batch processing is enabled with respect to a set of encrypted data records by techniques that ensure that each encrypted data record has its associated private data portion in a unique location relative to the other data records. The set of encrypted data records are then summed to generate a single encrypted data record against which the inferencing is done. In a first embodiment, the private data portions of interest are selectively and uniquely positioned at runtime (when the inferencing is being applied). In a second embodiment, the private data portions of interest are initially positioned with the data-at-rest, preferably in an off-line process; thereafter, at runtime individual encrypted data records are processed as necessary to adjust the private data portions to unique positions prior to batching.

Efficiently batching pre-encrypted data for homomorphic inference

An example operation may include one or more of generating, by a training participant client, a plurality of transaction proposals, each of the plurality of transaction proposals corresponding to a training iteration for machine learning model training related to stochastic gradient descent, the machine learning model training comprising a plurality of training iterations, the transaction proposals comprising a gradient calculation performed by the training participant client, transferring the plurality of transaction proposals to one or more endorser nodes or peers each comprising a verify gradient smart contract, executing, by each of the endorser nodes or peers, the verify gradient smart contract; and providing endorsements corresponding to the plurality of transaction proposals to the training participation client.

Efficient verification of machine learning applications

An example operation may include one or more of generating a hashed summary including hashes of one or more of a validation data set and hashes of data points chosen in previous iterations from producer nodes, and exposing the hashed summary to a plurality of producer nodes, receiving, iteratively, a plurality of requests from the plurality of producer nodes, respectively, where each request identifies a marginal value provided by a hash of a data sample available to a producer node, selecting a request received from a producer node based on a marginal value associated with the request, retrieving hashed data of the producer node associated with the selected request, and aggregating the hashed data of the producer node with the summary of hashes generated at one or more previous iterations to produce an updated summary, and storing the updated summary via a data block of a distributed ledger.

Artificial intelligence software marketplace

An example operation may include one or more of generating a plurality of successive data points of an iterative simulation based on predefined checkpoints, each data point identifying an evolving state of the iterative simulation with respect to a previous data point among the successive data points, transmitting a blockchain request for validating state data within a first data point among the plurality of successive data points to a first subset of endorsing nodes of a blockchain network, and transmitting a blockchain request for validating state data within a second data point among the plurality of successive data points to a second subset of endorsing nodes which are mutually exclusive from the first subset of endorsing nodes of the blockchain network for parallel endorsement of the first and second data points.

Distributed platform for computation and trusted validation

An example operation may include one or more of obtaining data of a simulation, identifying checkpoints within the simulation data, generating a plurality of sequential data structures based on the identified checkpoints, where each data structure identifies an evolving state of the simulation with respect to a previous data structure among the sequential data structures, and transmitting the generated sequential data structures to nodes of a blockchain network for inclusion in one or more data blocks within a hash-linked chain of data blocks.

A method, apparatus and computer program product for homomorphic inference on a decision tree (DT) model. In lieu of HE-based inferencing on the decision tree, the inferencing instead is performed on a neural network (NN), which acts as a surrogate. To this end, the neural network is trained to learn DT decision boundaries, preferably without using the original DT model data training points. During training, a random data set is applied to the DT, and expected outputs are recorded. This random data set and the expected outputs are then used to train the neural network such that the outputs of the neural network match the outputs expected from applying the original data set to the DT. Preferably, the neural network has low depth, just a few layers. HE-based inferencing on the decision tree is done using HE inferencing on the shallow neural network. The latter is computationally-efficient and is carried without the need for bootstrapping.

Decision tree-based inference on homomorphically-encrypted data without bootstrapping

An example operation may include one or more of generating, by a plurality of training participant clients, gradient calculations for machine learning model training, each of the training participant clients comprising a training dataset, converting, by a training aggregator coupled to the plurality of training participant clients, the gradient calculations to a plurality of transaction proposals, receiving, by one or more endorser nodes or peers of a blockchain network, the plurality of transaction proposals, executing, by each of the endorser nodes or peers, a verify gradient smart contract, and providing endorsements corresponding to the plurality of transaction proposals to the training aggregator.

Aggregated machine learning verification for database

This disclosure provides a method, apparatus and computer program product to create a full homomorphic encryption (FHE)-friendly machine learning model. The approach herein leverages a knowledge distillation framework wherein the FHE-friendly (student) ML model closely mimics the predictions of a more complex (teacher) model, wherein the teacher model is one that, relative to the student model, is more complex and that is pre-trained on large datasets. In the approach herein, the distillation framework uses the more complex teacher model to facilitate training of the FHE-friendly model, but using synthetically-generated training data in lieu of the original datasets used to train the teacher.

Depth-constrained knowledge distillation for inference on encrypted data

An example operation may include one or more of generating, by a training participant client comprising a training dataset, a plurality of transaction proposals that each correspond to a training iteration for machine learning model training related to stochastic gradient descent, the machine learning model training comprising a plurality of training iterations, the transaction proposals comprising a gradient calculation performed by the training participant client, a batch from the private dataset, a loss function, and an original model parameter, receiving, by one or more endorser nodes of peers of a blockchain network, the plurality of transaction proposals, and evaluating each transaction proposal.

A technique for computationally-efficient privacy-preserving homomorphic inferencing against a decision tree. Inferencing is carried out by a server against encrypted data points provided by a client. Fully homomorphic computation is enabled with respect to the decision tree by intelligently configuring the tree and the real number-valued features that are applied to the tree. To that end, and to the extent the decision tree is unbalanced, the server first balances the tree. A cryptographic packing scheme is then applied to the balanced decision tree and, in particular, to one or more entries in at least one of: an encrypted feature set, and a threshold data set, that are to be used during the decision tree evaluation process. Upon receipt of an encrypted data point, homomorphic inferencing on the configured decision tree is performed using a highly-accurate approximation comparator, which implements a 'soft' membership recursive computation on real numbers, all in an oblivious manner.

Privacy-enhanced decision tree-based inference on homomorphically-encrypted data

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Bronxville, NY, United States of America

Roman Vaculin