PET-Exchange: A Privacy Enhanced Trading Framework : A Framework for Limit-Order Matching using Homomorphic Encryption in Trading

Abstract: Over the recent decades, an increasing amount of new traders has entered the securities markets in order to trade securities such as stocks and bonds on electronic and physical exchanges. This increase in trader activity can largely be attributed to a simpler trading process including the growth of the electronic securities exchanges allowing for more dynamic and global trading platforms. Ever since their introduction, electronic exchanges have grown in terms of volume traded. The underlying trading mechanisms have mostly stayed the same over the years with some additions and improvements. However, over the recent decade, high-frequency traders (HFT) using algorithmic trading have shifted the playing field using practices that many consider unethical. Furthermore, insider trading continues to cause trust issues in certain trading platforms. Multiple solutions to these kinds of unethical trading behaviors have been proposed. Homomorphic encryption has been proposed as a potential preventative mechanism among the proposed solutions. This thesis analyses the properties and effects of a privacy-preserving framework for trading securities on an electronic stock exchange. The method used to evaluate the effects on trading was to implement a framework for handling trading and matching encrypted orders. The framework was then evaluated against its unencrypted counterpart to compare their performance properties in terms of volume handled, amount of orders matched, and timings of certain instructions. Finally, their security properties were analyzed to understand the proposed solution's potential impact on transparency, fairness, and opportunities for financial crime in an electronic securities exchange. The implementation was evaluated on its privacy-preserving properties by evaluating its ability to prevent information disclosure in trading processes. Furthermore, the performance of the implementation was evaluated using a generated trading session to simulate the market with sample trade data. Finally, from the proposed framework and the findings from this evaluation regarding privacy preservation and performance, a conclusion regarding its applicability as an alternative to off-exchange trading and preventative method against unfair practices and financial crime in trading is presented. The evaluation showed that the privacy-preserving and cryptographic properties of the suggested encrypted exchange were reasonably strong and were able to fulfill its goal of preventing unfair advantages in trading stemming from access to plaintext order information. However, the performance of the suggested implementation shows that more work needs to be performed for it to be viable in public electronic stock exchanges, although the solution could be suitable for small scale trading and privacy-preserving auctions.