Dynamic Order Execution Through Protected Data Segmentation
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Solution Overview
Problem
Current dynamic order management systems face issues with data security and inefficiencies due to reliance on phone calls and instant messaging, leading to leaks of confidential information, delays, and miscommunication, which can result in unfair advantages and unfulfilled orders.
Innovation Solution
A multi-system paradigm that bifurcates data into protected and unprotected categories, obfuscates sensitive information, and uses a centralized execution system to manage orders, reducing data interception and network congestion while ensuring secure and efficient execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If confidential information is transmitted during order management, then order execution can proceed, but data security is compromised due to information leaks
Solution Approach 1:
The system segments confidential order data into protected components that are separately managed. The execution system receives order requests and divides sensitive information (such as identity, reserve price, and order type) from unprotected data, allowing selective transmission and processing while maintaining security through spatial and functional separation of data elements
Solution Approach 2:
The execution system acts as an intermediary between the originator system and respondent system. It receives confidential order requests, processes them through protected pathways, and transmits only necessary unprotected data to respondents. This intermediary role prevents direct exposure of sensitive information while enabling order execution to proceed
2Speed
If real-time communication is used for order management, then execution speed improves, but network congestion and data interception risks increase
Solution Approach 1:
The system extracts and removes protected data from the transmission pathway between systems. By taking out confidential information from the data stream that would otherwise be transmitted across the network, the system eliminates the need for sensitive data to traverse potentially congested or insecure network paths, reducing both congestion risks and interception vulnerabilities
Solution Approach 2:
The execution system serves as a mediator that receives complete order data, separates protected from unprotected information, and transmits only the necessary unprotected portions through the network. This intermediary processing prevents sensitive data from being exposed to network congestion and interception risks while maintaining efficient real-time execution
3Productivity
If multiple systems access the same execution engine simultaneously, then order liquidity improves, but data transmission complexity increases
Solution Approach 1:
The system implements local quality by providing different data sets to different systems based on their specific needs and security clearances. The execution system transmits unprotected data to respondent systems while retaining protected data locally, allowing each system to access only the data quality and type appropriate for its function, thereby reducing overall transmission complexity while maintaining liquidity
Data Source
AI summary
A method for safeguarding data in dynamic relational order management involves receiving a data feed and a dynamic order with various order attributes, including a protected dataset and an unprotected dataset. The protected dataset, containing a request type and a reserve execution threshold, is obfuscated from display on a respondent system. Instructions are transmitted to the respondent system to present a proposal request dataset for executing the dynamic order, comprising the unprotected dataset and the data feed. A proposal response dataset is received from the respondent system, including first and second execution values. Adjusted values are generated based on the received data, and an execution action is performed when the adjusted execution values meet the adjusted reserve execution threshold.