Paper 2022/923

All for one and one for all: Fully decentralised privacy-preserving dark pool trading using multi-party computation

Abstract

Financial dark pool trading venues are designed to keep pre-trade order information secret so that it cannot be misused by others. However, dark pools are vulnerable to an operator misusing the information in their system. Prior work has used MPC to tackle this problem by assuming that the dark pool is operated by a small set of two or three MPC parties. However, this raises the question of who plays the role of these operating parties and whether this scenario could be applied in the real world. In this work, we implement an MPC-based dark pool trading venue with up to 100 parties. This configuration would allow a real-world implementation where the operating parties are the active participants that trade in the venue (i.e., a ``no operator'' model), or where the parties are the main stakeholders of the venue (e.g., members of a non-profit partnership such as Plato). We use AWS cloud to empirically test the performance of the system. Results demonstrate that the system can achieve trading throughput required for some real-world venues, while the cost of hosting the system is negligible compared with the savings expected from guaranteeing no information leakage.