Multi-Agent Reinforcement Learning for Online Food Delivery with Location Privacy Preservation

Abahussein, S; Ye, D; Zhu, C; Cheng, Z; Siddique, U; Shen, S

Multi-Agent Reinforcement Learning for Online Food Delivery with Location Privacy Preservation

Abahussein, S Ye, D

Zhu, C Cheng, Z Siddique, U Shen, S

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Information (Switzerland), 2023, 14, (11), pp. 597
Issue Date:: 2023-11-01

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Field	Value	Language
dc.contributor.author	Abahussein, S
dc.contributor.author	Ye, D https://orcid.org/0000-0002-7561-0992
dc.contributor.author	Zhu, C
dc.contributor.author	Cheng, Z
dc.contributor.author	Siddique, U
dc.contributor.author	Shen, S
dc.date.accessioned	2024-03-28T02:59:38Z
dc.date.available	2024-03-28T02:59:38Z
dc.date.issued	2023-11-01
dc.identifier.citation	Information (Switzerland), 2023, 14, (11), pp. 597
dc.identifier.issn	2078-2489
dc.identifier.issn	2078-2489
dc.identifier.uri	http://hdl.handle.net/10453/177324
dc.description.abstract	Online food delivery services today are considered an essential service that gets significant attention worldwide. Many companies and individuals are involved in this field as it offers good income and numerous jobs to the community. In this research, we consider the problem of online food delivery services and how we can increase the number of received orders by couriers and thereby increase their income. Multi-agent reinforcement learning (MARL) is employed to guide the couriers to areas with high demand for food delivery requests. A map of the city is divided into small grids, and each grid represents a small area of the city that has different demand for online food delivery orders. The MARL agent trains and learns which grid has the highest demand and then selects it. Thus, couriers can get more food delivery orders and thereby increase long-term income. While increasing the number of received orders is important, protecting customer location is also essential. Therefore, the Protect User Location Method (PULM) is proposed in this research in order to protect customer location information. The PULM injects differential privacy (DP) Laplace noise based on two parameters: city area size and customer frequency of online food delivery orders. We use two datasets—Shenzhen, China, and Iowa, USA—to demonstrate the results of our experiments. The results show an increase in the number of received orders in the Shenzhen and Iowa City datasets. We also show the similarity and data utility of courier trajectories after we use our obfuscation (PULM) method.
dc.language	en
dc.publisher	MDPI
dc.relation.ispartof	Information (Switzerland)
dc.relation.isbasedon	10.3390/info14110597
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	08 Information and Computing Sciences
dc.subject.classification	46 Information and computing sciences
dc.title	Multi-Agent Reinforcement Learning for Online Food Delivery with Location Privacy Preservation
dc.type	Journal Article
utslib.citation.volume	14
utslib.for	08 Information and Computing Sciences
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	University of Technology Sydney/Strength - CCSP - Centre for Cyber Security and Privacy
utslib.copyright.status	open_access	*
dc.date.updated	2024-03-28T02:59:36Z
pubs.issue	11
pubs.publication-status	Published
pubs.volume	14
utslib.citation.issue	11

Abstract:

Online food delivery services today are considered an essential service that gets significant attention worldwide. Many companies and individuals are involved in this field as it offers good income and numerous jobs to the community. In this research, we consider the problem of online food delivery services and how we can increase the number of received orders by couriers and thereby increase their income. Multi-agent reinforcement learning (MARL) is employed to guide the couriers to areas with high demand for food delivery requests. A map of the city is divided into small grids, and each grid represents a small area of the city that has different demand for online food delivery orders. The MARL agent trains and learns which grid has the highest demand and then selects it. Thus, couriers can get more food delivery orders and thereby increase long-term income. While increasing the number of received orders is important, protecting customer location is also essential. Therefore, the Protect User Location Method (PULM) is proposed in this research in order to protect customer location information. The PULM injects differential privacy (DP) Laplace noise based on two parameters: city area size and customer frequency of online food delivery orders. We use two datasets—Shenzhen, China, and Iowa, USA—to demonstrate the results of our experiments. The results show an increase in the number of received orders in the Shenzhen and Iowa City datasets. We also show the similarity and data utility of courier trajectories after we use our obfuscation (PULM) method.

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http://hdl.handle.net/10453/177324