Advancing state-of-the-art techniques in:

Secure multiparty computation (MPC)

Fully homomorphic encryption (FHE)

Privacy-preserving machine learning (PPML)

Privacy-preserving federated learning (PPFL)

Zero-knowledge proofs (ZK)

Differential privacy (DP)

Quantum cryptography (QC)

Privacy-preserving blockchains

Manuscripts

Paving the Way towards 800 Gbps Quantum-Secured Optical Channel Deployment in Mission-Critical Environments.
Farzam Toudeh-Fallah, Marco Pistoia, Yasushi Kawakura, Navid Moazzami, David H. Kramer, Robert I. Woodward, Greg Sysak, Benny John, Omar Amer, Antigoni O. Polychroniadou, Jeffrey Lyon, Suresh Shetty, Tulasi D. Movva, Sudhir Upadhyay, Monik R. Behera, Joseph A. Dolphin, Paul A. Haigh, James F. Dynes and Andrew J. Shields

Prio+: Privacy Preserving Aggregate Statistics via Boolean Shares.
Surya Addanki, Kevin Garbe, Eli Jaffe, Rafail Ostrovsky and Antigoni Polychroniadou

Phoenix: Secure Computation in an Unstable Network with Dropouts and Comebacks.
Ivan Damgård, Daniel Escudero and Antigoni Polychroniadou

This [paper/presentation] was prepared for informational purposes by the [Artificial Intelligence Research] group of JPMorgan Chase & Co and its affiliates (“JP Morgan”), and is not a product of the Research Department of JP Morgan. JP Morgan makes no representation and warranty whatsoever and disclaims all liability, for the completeness, accuracy or reliability of the information contained herein. This document is not intended as investment research or investment advice, or a recommendation, offer or solicitation for the purchase or sale of any security, financial instrument, financial product or service, or to be used in any way for evaluating the merits of participating in any transaction, and shall not constitute a solicitation under any jurisdiction or to any person if such solicitation under such jurisdiction or to such person would be unlawful.

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