Privacy-Preserving Online Ride-Hailing Matching System with an Untrusted Server

Hongcheng Xie; Zizhuo Chen; Yu Guo; Qin Liu; Xiaohua Jia

doi:10.1007/978-3-031-23020-2_24

Privacy-Preserving Online Ride-Hailing Matching System with an Untrusted Server

Hongcheng Xie^*
, Zizhuo Chen
, Yu Guo
, Qin Liu
, Xiaohua Jia

^*Corresponding author for this work

Department of Computer Science

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

1 Citation (Scopus)

Abstract

With the popularity of Online Ride-Hailing (ORH) service, there are growing concerns about location privacy because the taxis and passengers need to upload their locations to the service provider. These locations can be used to infer the users’ personal information. In this paper, we propose a privacy-preserving online ride-hailing matching system, which allows an untrusted service provider to calculate the distances between the taxis and one passenger and find the nearest taxi by itself while protecting the users’ location privacy. To calculate the distances in road networks, we leverage Road Network Embedding (RNE) in our proposed system. We propose a secure distance calculation scheme to conduct RNE distance calculation securely. In this scheme, we redesign Property-Preserving Hash (PPH) with Pseudo-Random Functions (PRF) and use PRF-based PPH to calculate the distance between two RNE location vectors securely. To enhance security, we embed the partition ID and generation time in PRF-based PPH ciphertext to limit the ciphertext match-ability. Our security analysis and experimental evaluation show that our proposed system is secure and efficient.

Original language	English
Title of host publication	Network and System Security
Subtitle of host publication	16th International Conference, NSS 2022, Denarau Island, Fiji, December 9–12, 2022, Proceedings
Editors	Xingliang Yuan, Guangdong Bai, Cristina Alcaraz, Suryadipta Majumdar
Place of Publication	Cham
Publisher	Springer
Pages	429-442
ISBN (Electronic)	978-3-031-23020-2
ISBN (Print)	978-3-031-23019-6
DOIs	https://doi.org/10.1007/978-3-031-23020-2_24
Publication status	Published - 2022
Event	16th International Conference on Network and System Security (NSS 2022) - Sheraton Fiji Golf & Beach Resort, Denarau Island, Fiji Duration: 9 Dec 2022 → 12 Dec 2022 http://nsclab.org/nss2022/

Publication series

Name	Lecture Notes in Computer Science
Volume	13787
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Conference on Network and System Security (NSS 2022)
Place	Fiji
City	Denarau Island
Period	9/12/22 → 12/12/22
Internet address	http://nsclab.org/nss2022/

Funding

This work is financially supported by NSFC under Grant No. 62102035, and HKRGC under Grant No. CityU 11213920.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

RGC Funding Information

RGC-funded

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10.1007/978-3-031-23020-2_24

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Cite this

Xie, H., Chen, Z., Guo, Y., Liu, Q., & Jia, X. (2022). Privacy-Preserving Online Ride-Hailing Matching System with an Untrusted Server. In X. Yuan, G. Bai, C. Alcaraz, & S. Majumdar (Eds.), Network and System Security: 16th International Conference, NSS 2022, Denarau Island, Fiji, December 9–12, 2022, Proceedings (pp. 429-442). (Lecture Notes in Computer Science; Vol. 13787). Springer . https://doi.org/10.1007/978-3-031-23020-2_24

Xie, Hongcheng ; Chen, Zizhuo ; Guo, Yu et al. / Privacy-Preserving Online Ride-Hailing Matching System with an Untrusted Server. Network and System Security: 16th International Conference, NSS 2022, Denarau Island, Fiji, December 9–12, 2022, Proceedings. editor / Xingliang Yuan ; Guangdong Bai ; Cristina Alcaraz ; Suryadipta Majumdar. Cham : Springer , 2022. pp. 429-442 (Lecture Notes in Computer Science).

@inproceedings{1e56713793524ce2a3b35767d59253b1,

title = "Privacy-Preserving Online Ride-Hailing Matching System with an Untrusted Server",

abstract = "With the popularity of Online Ride-Hailing (ORH) service, there are growing concerns about location privacy because the taxis and passengers need to upload their locations to the service provider. These locations can be used to infer the users{\textquoteright} personal information. In this paper, we propose a privacy-preserving online ride-hailing matching system, which allows an untrusted service provider to calculate the distances between the taxis and one passenger and find the nearest taxi by itself while protecting the users{\textquoteright} location privacy. To calculate the distances in road networks, we leverage Road Network Embedding (RNE) in our proposed system. We propose a secure distance calculation scheme to conduct RNE distance calculation securely. In this scheme, we redesign Property-Preserving Hash (PPH) with Pseudo-Random Functions (PRF) and use PRF-based PPH to calculate the distance between two RNE location vectors securely. To enhance security, we embed the partition ID and generation time in PRF-based PPH ciphertext to limit the ciphertext match-ability. Our security analysis and experimental evaluation show that our proposed system is secure and efficient.",

author = "Hongcheng Xie and Zizhuo Chen and Yu Guo and Qin Liu and Xiaohua Jia",

year = "2022",

doi = "10.1007/978-3-031-23020-2\_24",

language = "English",

isbn = "978-3-031-23019-6",

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editor = "Xingliang Yuan and Guangdong Bai and Cristina Alcaraz and Suryadipta Majumdar",

booktitle = "Network and System Security",

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Xie, H , Chen, Z, Guo, Y, Liu, Q & Jia, X 2022, Privacy-Preserving Online Ride-Hailing Matching System with an Untrusted Server. in X Yuan, G Bai, C Alcaraz & S Majumdar (eds), Network and System Security: 16th International Conference, NSS 2022, Denarau Island, Fiji, December 9–12, 2022, Proceedings. Lecture Notes in Computer Science, vol. 13787, Springer , Cham, pp. 429-442, 16th International Conference on Network and System Security (NSS 2022), Denarau Island, Fiji, 9/12/22. https://doi.org/10.1007/978-3-031-23020-2_24

Privacy-Preserving Online Ride-Hailing Matching System with an Untrusted Server. / Xie, Hongcheng ; Chen, Zizhuo; Guo, Yu et al.
Network and System Security: 16th International Conference, NSS 2022, Denarau Island, Fiji, December 9–12, 2022, Proceedings. ed. / Xingliang Yuan; Guangdong Bai; Cristina Alcaraz; Suryadipta Majumdar. Cham: Springer , 2022. p. 429-442 (Lecture Notes in Computer Science; Vol. 13787).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Privacy-Preserving Online Ride-Hailing Matching System with an Untrusted Server

AU - Xie, Hongcheng

AU - Chen, Zizhuo

AU - Guo, Yu

AU - Liu, Qin

AU - Jia, Xiaohua

PY - 2022

Y1 - 2022

N2 - With the popularity of Online Ride-Hailing (ORH) service, there are growing concerns about location privacy because the taxis and passengers need to upload their locations to the service provider. These locations can be used to infer the users’ personal information. In this paper, we propose a privacy-preserving online ride-hailing matching system, which allows an untrusted service provider to calculate the distances between the taxis and one passenger and find the nearest taxi by itself while protecting the users’ location privacy. To calculate the distances in road networks, we leverage Road Network Embedding (RNE) in our proposed system. We propose a secure distance calculation scheme to conduct RNE distance calculation securely. In this scheme, we redesign Property-Preserving Hash (PPH) with Pseudo-Random Functions (PRF) and use PRF-based PPH to calculate the distance between two RNE location vectors securely. To enhance security, we embed the partition ID and generation time in PRF-based PPH ciphertext to limit the ciphertext match-ability. Our security analysis and experimental evaluation show that our proposed system is secure and efficient.

AB - With the popularity of Online Ride-Hailing (ORH) service, there are growing concerns about location privacy because the taxis and passengers need to upload their locations to the service provider. These locations can be used to infer the users’ personal information. In this paper, we propose a privacy-preserving online ride-hailing matching system, which allows an untrusted service provider to calculate the distances between the taxis and one passenger and find the nearest taxi by itself while protecting the users’ location privacy. To calculate the distances in road networks, we leverage Road Network Embedding (RNE) in our proposed system. We propose a secure distance calculation scheme to conduct RNE distance calculation securely. In this scheme, we redesign Property-Preserving Hash (PPH) with Pseudo-Random Functions (PRF) and use PRF-based PPH to calculate the distance between two RNE location vectors securely. To enhance security, we embed the partition ID and generation time in PRF-based PPH ciphertext to limit the ciphertext match-ability. Our security analysis and experimental evaluation show that our proposed system is secure and efficient.

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U2 - 10.1007/978-3-031-23020-2_24

DO - 10.1007/978-3-031-23020-2_24

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-3-031-23019-6

T3 - Lecture Notes in Computer Science

SP - 429

EP - 442

BT - Network and System Security

A2 - Yuan, Xingliang

A2 - Bai, Guangdong

A2 - Alcaraz, Cristina

A2 - Majumdar, Suryadipta

PB - Springer

CY - Cham

T2 - 16th International Conference on Network and System Security (NSS 2022)

Y2 - 9 December 2022 through 12 December 2022

ER -

Xie H , Chen Z, Guo Y, Liu Q, Jia X. Privacy-Preserving Online Ride-Hailing Matching System with an Untrusted Server. In Yuan X, Bai G, Alcaraz C, Majumdar S, editors, Network and System Security: 16th International Conference, NSS 2022, Denarau Island, Fiji, December 9–12, 2022, Proceedings. Cham: Springer . 2022. p. 429-442. (Lecture Notes in Computer Science). Epub 2022 Dec 7. doi: 10.1007/978-3-031-23020-2_24

Privacy-Preserving Online Ride-Hailing Matching System with an Untrusted Server

Abstract

Publication series

Conference

Funding

UN SDGs

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GRF: A Blockchain-Based Federated Crowdsourcing Platform for Privacy-Preserving Applications

Cite this

Privacy-Preserving Online Ride-Hailing Matching System with an Untrusted Server

Abstract

Publication series

Conference

Funding

UN SDGs

RGC Funding Information

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Projects

GRF: A Blockchain-Based Federated Crowdsourcing Platform for Privacy-Preserving Applications

Cite this