Privacy-preserving auction using multi-party computation

When it comes to trading and auctions, a party would not want to reveal their intention to buy or sell, as other parties may use the information to influence the market (such as sellers driving market prices up when they know an incoming buyer intends to purchase a large volume of items). Traditiona...

Full description

Saved in:
Bibliographic Details
Main Author: Teo, Priscilla Qiu Yee
Other Authors: Sourav Sen Gupta
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2021
Subjects:
Online Access:https://hdl.handle.net/10356/148014
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-148014
record_format dspace
spelling sg-ntu-dr.10356-1480142021-04-22T05:01:03Z Privacy-preserving auction using multi-party computation Teo, Priscilla Qiu Yee Sourav Sen Gupta School of Computer Science and Engineering sg.sourav@ntu.edu.sg Engineering::Computer science and engineering When it comes to trading and auctions, a party would not want to reveal their intention to buy or sell, as other parties may use the information to influence the market (such as sellers driving market prices up when they know an incoming buyer intends to purchase a large volume of items). Traditional approaches have relied on a trusted third party, which are susceptible to illegally performing trades with their insider information for their personal gain. To eliminate the reliance on a human intermediary (whether that is a trusted broker or a system operator for electronic trading means), one possible way is to conduct an auction using Multi-Party Computation (MPC). In short, the MPC technique shares inputs across multiple parties – where no individual parties have the full input, but all the parties will jointly compute with functions to get the output. Knowing more about MPC and their current works on auction, this project sees the use of MPC technique to perform an auction with privacy-preserving properties. As this was a pair project, my role is to create an algorithm to trade multiple volumes of items between buyers and sellers. The MPC algorithm will be compared with the same algorithm in cleartext form (that is, without regards for maintaining privacy of inputs or variables inside the algorithm), to reflect the trade-offs between efficiency favouring the cleartext algorithm, and security which should be achieved in the MPC algorithm. The MPC algorithm was created with 2 buyers and 2 sellers in mind, and we will analyse the impact on algorithmic complexity with more parties involved. Lastly, the algorithm will be integrated with the system architecture proposed by my teammate. This section will be brief to reflect the end-product of the project. Bachelor of Engineering (Computer Science) 2021-04-22T05:01:03Z 2021-04-22T05:01:03Z 2021 Final Year Project (FYP) Teo, P. Q. Y. (2021). Privacy-preserving auction using multi-party computation. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148014 https://hdl.handle.net/10356/148014 en SCSE20-0538 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering
spellingShingle Engineering::Computer science and engineering
Teo, Priscilla Qiu Yee
Privacy-preserving auction using multi-party computation
description When it comes to trading and auctions, a party would not want to reveal their intention to buy or sell, as other parties may use the information to influence the market (such as sellers driving market prices up when they know an incoming buyer intends to purchase a large volume of items). Traditional approaches have relied on a trusted third party, which are susceptible to illegally performing trades with their insider information for their personal gain. To eliminate the reliance on a human intermediary (whether that is a trusted broker or a system operator for electronic trading means), one possible way is to conduct an auction using Multi-Party Computation (MPC). In short, the MPC technique shares inputs across multiple parties – where no individual parties have the full input, but all the parties will jointly compute with functions to get the output. Knowing more about MPC and their current works on auction, this project sees the use of MPC technique to perform an auction with privacy-preserving properties. As this was a pair project, my role is to create an algorithm to trade multiple volumes of items between buyers and sellers. The MPC algorithm will be compared with the same algorithm in cleartext form (that is, without regards for maintaining privacy of inputs or variables inside the algorithm), to reflect the trade-offs between efficiency favouring the cleartext algorithm, and security which should be achieved in the MPC algorithm. The MPC algorithm was created with 2 buyers and 2 sellers in mind, and we will analyse the impact on algorithmic complexity with more parties involved. Lastly, the algorithm will be integrated with the system architecture proposed by my teammate. This section will be brief to reflect the end-product of the project.
author2 Sourav Sen Gupta
author_facet Sourav Sen Gupta
Teo, Priscilla Qiu Yee
format Final Year Project
author Teo, Priscilla Qiu Yee
author_sort Teo, Priscilla Qiu Yee
title Privacy-preserving auction using multi-party computation
title_short Privacy-preserving auction using multi-party computation
title_full Privacy-preserving auction using multi-party computation
title_fullStr Privacy-preserving auction using multi-party computation
title_full_unstemmed Privacy-preserving auction using multi-party computation
title_sort privacy-preserving auction using multi-party computation
publisher Nanyang Technological University
publishDate 2021
url https://hdl.handle.net/10356/148014
_version_ 1698713655039754240