A dark pool is a private forum for trading financial instruments such as equities and derivatives. The service is offered to traders, often representing large financial institutions, who aim to make large trades without telegraphing their intentions in a public venue such as the New York Stock Exchange or NASDAQ. Internally, the dark pool operates just like a public exchange, but the order book, a list of offers to buy and sell, is not visible to any of the participants. Only when matching orders are found the trade is executed and reported externally. Because the large orders are not visible to others, the risk of significant price moves is reduced. 

The operators of dark pools are trustworthy, but traders are notoriously suspicious. Traders are concerned about any ``leakage'' of information that might alert others to their intentions and cause price moves against them. The operator sees all orders posted by all participants, including sensitive information such as the volume of the order and the bid or ask price. Because the operator of the dark pool is usually a large financial institution with its own investments in the assets being traded, it may have conflicts of interest with the clients it serves in the dark pool. While such conflicts are mitigated by law and regulation, they continue to exist.

We propose new mechanism that reduce the need of traders to trust the operator of the dark pool. By adopting cryptographic techniques, we achieve a fully private and secure marketplace where sellers and buyers interact with the operator without exposing the volumes or prices of their orders to the operator or to any other counter party. These values remain secret until a matching order is found, and only afterward it will be revealed publicly. This provides a strictly stronger privacy guarantee compared to ordinary dark pools, and may lead to an increase in the trust of the participants to the system and reduce tension between participants and the operator.

Privacy Preserving Dark Pools

Learning and Adaptation

technical paper

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). The aim of the joint conference is to provide a single, high-profile, internationally-respected archival forum for scientific research in the theory and practice of autonomous agents and multi-agent systems.

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AAMAS 2020

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). 

Tucker Balch

1

Presentations

Privacy Preserving Dark Pools