Paper 2024/461

Atlas-X Equity Financing: Unlocking New Methods to Securely Obfuscate Axe Inventory Data Based on Differential Privacy

Antigoni Polychroniadou, J.P. Morgan AI Research
Gabriele Cipriani, J.P. Morgan
Richard Hua, J.P. Morgan
Tucker Balch, J.P. Morgan AI Research
Abstract

Banks publish daily a list of available securities/assets (axe list) to selected clients to help them effectively locate Long (buy) or Short (sell) trades at reduced financing rates. This reduces costs for the bank, as the list aggregates the bank's internal firm inventory per asset for all clients of long as well as short trades. However, this is somewhat problematic: (1) the bank's inventory is revealed; (2) trades of clients who contribute to the aggregated list, particularly those deemed large, are revealed to other clients. Clients conducting sizable trades with the bank and possessing a portion of the aggregated asset exceeding $50\%$ are considered to be concentrated clients. This could potentially reveal a trading concentrated client's activity to their competitors, thus providing an unfair advantage over the market. Atlas-X Axe Obfuscation, powered by new differential private methods, enables a bank to obfuscate its published axe list on a daily basis while under continual observation, thus maintaining an acceptable inventory Profit and Loss (P\&L) cost pertaining to the noisy obfuscated axe list while reducing the clients' trading activity leakage. Our main differential private innovation is a differential private aggregator for streams (time series data) of both positive and negative integers under continual observation. For the last two years, Atlas-X system has been live in production across three major regions—USA, Europe, and Asia—at J.P. Morgan, a major financial institution, facilitating significant profitability. To our knowledge, it is the first differential privacy solution to be deployed in the financial sector. We also report benchmarks of our algorithm based on (anonymous) real and synthetic data to showcase the quality of our obfuscation and its success in production.

Metadata
Available format(s)
PDF
Category
Applications
Publication info
Published elsewhere. AAMAS 2024
Keywords
Differential privacyTime-series dataMarketsprivacy under continuous observation
Contact author(s)
antigonipoly @ gmail com
gabriele cipriani @ jpmorgan com
richard hua @ jpmorgan com
History
2024-03-22: approved
2024-03-19: received
See all versions
Short URL
https://ia.cr/2024/461
License
Creative Commons Attribution
CC BY

BibTeX

@misc{cryptoeprint:2024/461,
      author = {Antigoni Polychroniadou and Gabriele Cipriani and Richard Hua and Tucker Balch},
      title = {Atlas-X Equity Financing: Unlocking New Methods to Securely Obfuscate Axe Inventory Data Based on Differential Privacy},
      howpublished = {Cryptology ePrint Archive, Paper 2024/461},
      year = {2024},
      note = {\url{https://eprint.iacr.org/2024/461}},
      url = {https://eprint.iacr.org/2024/461}
}
Note: In order to protect the privacy of readers, eprint.iacr.org does not use cookies or embedded third party content.