Jpmorgan Chase Bank, N.a.

Vamsi Krishna Potluru

Tucker Richard Balch

Manuela Veloso

Prashant Papiahgari Reddy

Maria Manuela Veloso

Robert Elliott Tillman

Niccolo Dalmasso

Mohsen Ghassemi

Eleonora Kreacic

A method and system for watermarking a dataset generated by a source system are disclosed. The method includes acquiring the dataset, distributing data elements included in the dataset over a range, and dividing the range into multiple bins according to a scheme. The method further includes designating each of the bins as a first or second type, tagging each data element according to according to a bin type of a bin the respective data element falls into. For each data element included in a bin of the second type, selecting a new value by sampling within a nearest bin of the first type and replacing the respective data element with a replacement data element including the new value, and watermarking each of the data elements originally included in the bins of the first type and replacement data elements for generating a watermarked dataset.

System and method for watermarking tabular data while obscuring underlying data for improving data integrity and security

A method for preserving privacy with respect to modeling event sequence data is provided. The method includes: receiving information about a sequence of events; modeling the event sequence by a Hawkes process that has an intensity that includes an exogenous base intensity rate and an indigenous component that has an excitation rate and a decay rate; analyzing the received information; and determining estimated values of the exogenous base intensity rate and the excitation rate, such that an accuracy of the estimates corresponds to a length of time over which the sequence of events is observed. Differential privacy is introduced by adding noise to the sequence of events in order to preserve the privacy of individuals associated with the events, and a cost of the differential privacy is expressible as an additional length of observation time required to ensure the accuracy of the estimates.

Method and system for differentially private learning of Hawkes processes

A method and a system for using a graph neural network framework to implement a link prediction in a multiplex network environment is provided. The method includes: identifying a plurality of layers of a multiplex network, each respective layer including a respective plurality of nodes; for each node included in at least a first layer, providing, by a structural node label and determining a common embedding across all of the plurality of layers and an individual embedding for each individual layer; using a k-nearest approach to select a subset of the plurality of layers for performing link prediction with respect to each layer based on the determined embeddings; and performing a link prediction by determining a respective feed-forward network with respect to each layer included in the selected subset.

Method and system for link prediction in large multiplex networks

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New York, NY, United States of America

Vamsi Krishna Potluru