Jpmorgan Chase Bank, N.a.

Antigoni Ourania Polychroniadou

Tucker Richard Balch

Benjamin E Diamond

Yue Guo

Gabriele Cipriani

Marco Pistoia

Maria Manuela Veloso

Farzam Toudeh-Fallah

Hans Buehler

Gilad Asharov

Richard Hua

A method for providing a secure aggregation protocol that facilitates federated learning of machine learning models while protecting privacy of individual data sets associated with participants is provided. The method includes: a setup phase by which public key—secret key pairs are used to verify signatures and facilitate secure transmissions of encrypted messages that include datasets to be aggregated; and an aggregation phase by which a random oracle/hash function is used to facilitate the aggregation of newly submitted datasets into the preexisting aggregated dataset.

Method and system for secure aggregation protocol for protecting privacy of individual data sets

A method for learning a shared machine learning model while preserving privacy of individual participants is provided. The method includes: receiving, from each of a group of users, an encrypted user input; when a number of user inputs is greater than or equal to a threshold, transmitting, to each user, a list of the group of users; receiving, from each user, a message indicating a mutual agreement regarding a shared secret among the group; and when a number of received messages indicating the mutual agreement is greater than or equal to the threshold, determining information about the shared machine learning model by combining the received encrypted user inputs. The shared machine learning model facilitates a secure multi-party computation of a function that generates an updated version of the shared machine learning model.

Secure aggregation of information using federated learning

A method for privacy-preserving inventory matching may include: (1) receiving a plurality of axe submissions; (2) arranging the parties into data structures based on a direction in the party's axe submission; (3) sending each party's commitment to the other party; (4) receiving, from each party, output secret-shares of an arithmetized comparison circuit; (5) verifying that the output secret-shares of the arithmetized comparison circuit received from the parties match commitments to the output secret-shares sent by the respective opposite party; (6) identifying a minimal party based on the outputs of the arithmetized comparison circuit; (7) generating and sending a proof of the minimal party identification to the minimal party; (8) receiving a minimal quantity integer from the minimal party; (9) revealing the minimal quantity integer to the first party and the second party; and (10) executing the trade for the minimal quantity integer.

Systems and methods for privacy-preserving inventory matching with security against malicious adversaries

A method and a system for using differential privacy techniques to provide axe obfuscation with respect to information included in an inventory axe list of available securities is provided. The method includes: obtaining first information to be included in a first inventory axe list to be published on a particular day; retrieving second information included in a second inventory axe list that was published on the previous day and/or several previous days; obfuscating the obtained first information by applying an algorithm based on the difference between the first information and the second information; and publishing the first inventory axe list by transmitting the obfuscated first information to a plurality of intended recipients. The quality of obfuscation may be measured and controlled as a function of desired privacy level and potential cost.

Differential privacy for axe obfuscation

Systems and methods for privacy-preserving inventory matching are disclosed. In one embodiment, in an information processing apparatus comprising at least one computer processor, a method for inventory matching may include: (1) receiving, from each of a plurality of clients, a masked submission comprising an identification of at least one security to buy or sell and a desired quantity to buy or sell; (2) aggregating the masked submissions resulting in a sum of the desired quantities to buy or sell; (3) matching at least two of the clients to conduct a transaction based on aggregation and their respective masked submissions; and (4) conducing the transaction between the matched clients.

Systems and methods for privacy-preserving inventory matching

Systems and methods for processing and transmission of encrypted data are provided. The method includes: encrypting a first data set; encapsulating the encrypted first data set in a protective layer; and transmitting the encapsulated encrypted first data set to a destination over one or more communication channels. The encrypting is performed by using a homomorphic encryption (HE) technique. The encapsulating is performed by using a quantum key distribution (QKD) encapsulation technique to generate a QKD-protected layer. The communication channels may include a classical/non-quantum channel over which the QKD-encapsulated encrypted first set of data is transmitted and a quantum channel over which a quantum key distribution is conducted, or a single communication channel to conduct both.

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New York, NY, United States of America

Antigoni Ourania Polychroniadou