Privacy-Enhanced and Practical Truth Discovery in Two-Server Mobile Crowdsensing

Research output: Contribution to journalJournal articlepeer-review

Standard

Privacy-Enhanced and Practical Truth Discovery in Two-Server Mobile Crowdsensing. / Wu, Haiqin; Wang, Liangmin; Cheng, Ke; Yang, Dejun; Tang, Jian; Xue, Guoliang.

In: IEEE Transactions on Network Science and Engineering, Vol. 9, No. 3, 2022, p. 1740-1755.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Wu, H, Wang, L, Cheng, K, Yang, D, Tang, J & Xue, G 2022, 'Privacy-Enhanced and Practical Truth Discovery in Two-Server Mobile Crowdsensing', IEEE Transactions on Network Science and Engineering, vol. 9, no. 3, pp. 1740-1755. https://doi.org/10.1109/TNSE.2022.3151228

APA

Wu, H., Wang, L., Cheng, K., Yang, D., Tang, J., & Xue, G. (2022). Privacy-Enhanced and Practical Truth Discovery in Two-Server Mobile Crowdsensing. IEEE Transactions on Network Science and Engineering, 9(3), 1740-1755. https://doi.org/10.1109/TNSE.2022.3151228

Vancouver

Wu H, Wang L, Cheng K, Yang D, Tang J, Xue G. Privacy-Enhanced and Practical Truth Discovery in Two-Server Mobile Crowdsensing. IEEE Transactions on Network Science and Engineering. 2022;9(3):1740-1755. https://doi.org/10.1109/TNSE.2022.3151228

Author

Wu, Haiqin ; Wang, Liangmin ; Cheng, Ke ; Yang, Dejun ; Tang, Jian ; Xue, Guoliang. / Privacy-Enhanced and Practical Truth Discovery in Two-Server Mobile Crowdsensing. In: IEEE Transactions on Network Science and Engineering. 2022 ; Vol. 9, No. 3. pp. 1740-1755.

Bibtex

@article{4e7d4e778e1e444a8918ab3a5bd27e0e,
title = "Privacy-Enhanced and Practical Truth Discovery in Two-Server Mobile Crowdsensing",
abstract = "In mobile crowdsensing, truth discovery (TD) enables a crowdsensing server to extract truthful information from possibly conflicting crowdsensing data. TD provides a more accurate truth estimation than traditional truth inference methods like majority voting and averaging. However, there still exist crucial data privacy (including sensory data, inferred truths, and intermediates) and practicability (e.g., efficiency, utility, and non-interaction) concerns in real-world crowdsensing applications. Existing researches either fail to provide adequate data privacy protection throughout the entire TD procedure or suffer from low practicability. In this paper, we propose two schemes: a basic privacy-aware TD scheme (BPTD) and a privacy-enhanced TD scheme (PETD) with two servers for mobile crowdsensing, comprehensively considering both privacy and practicability. BPTD is straightforwardly conducted on shared data with few user-side interactions, while achieving high efficiency. To further liberate mobile users and prevent disclosure of the intermediates, PETD incorporates a novel partial decryption-based Paillier Cryptosystem to work with secret sharing, offering enhanced privacy protection without relying on any user-side involvement. Additionally, we improve the efficiency of PETD via data packing. Security analysis shows the desired privacy goals. Compared to prior studies with the best security guarantees, our extensive experiments demonstrate a comparable and superior performance regarding different metrics.",
keywords = "Costs, Crowdsensing, Cryptography, data aggregation, Data privacy, Mobile crowdsensing, Paillier Cryptosystem, Privacy, Reliability, secret sharing, Servers, truth discovery",
author = "Haiqin Wu and Liangmin Wang and Ke Cheng and Dejun Yang and Jian Tang and Guoliang Xue",
note = "Publisher Copyright: IEEE",
year = "2022",
doi = "10.1109/TNSE.2022.3151228",
language = "English",
volume = "9",
pages = "1740--1755",
journal = "IEEE Transactions on Network Science and Engineering",
issn = "2327-4697",
publisher = "IEEE Computer Society Press",
number = "3",

}

RIS

TY - JOUR

T1 - Privacy-Enhanced and Practical Truth Discovery in Two-Server Mobile Crowdsensing

AU - Wu, Haiqin

AU - Wang, Liangmin

AU - Cheng, Ke

AU - Yang, Dejun

AU - Tang, Jian

AU - Xue, Guoliang

N1 - Publisher Copyright: IEEE

PY - 2022

Y1 - 2022

N2 - In mobile crowdsensing, truth discovery (TD) enables a crowdsensing server to extract truthful information from possibly conflicting crowdsensing data. TD provides a more accurate truth estimation than traditional truth inference methods like majority voting and averaging. However, there still exist crucial data privacy (including sensory data, inferred truths, and intermediates) and practicability (e.g., efficiency, utility, and non-interaction) concerns in real-world crowdsensing applications. Existing researches either fail to provide adequate data privacy protection throughout the entire TD procedure or suffer from low practicability. In this paper, we propose two schemes: a basic privacy-aware TD scheme (BPTD) and a privacy-enhanced TD scheme (PETD) with two servers for mobile crowdsensing, comprehensively considering both privacy and practicability. BPTD is straightforwardly conducted on shared data with few user-side interactions, while achieving high efficiency. To further liberate mobile users and prevent disclosure of the intermediates, PETD incorporates a novel partial decryption-based Paillier Cryptosystem to work with secret sharing, offering enhanced privacy protection without relying on any user-side involvement. Additionally, we improve the efficiency of PETD via data packing. Security analysis shows the desired privacy goals. Compared to prior studies with the best security guarantees, our extensive experiments demonstrate a comparable and superior performance regarding different metrics.

AB - In mobile crowdsensing, truth discovery (TD) enables a crowdsensing server to extract truthful information from possibly conflicting crowdsensing data. TD provides a more accurate truth estimation than traditional truth inference methods like majority voting and averaging. However, there still exist crucial data privacy (including sensory data, inferred truths, and intermediates) and practicability (e.g., efficiency, utility, and non-interaction) concerns in real-world crowdsensing applications. Existing researches either fail to provide adequate data privacy protection throughout the entire TD procedure or suffer from low practicability. In this paper, we propose two schemes: a basic privacy-aware TD scheme (BPTD) and a privacy-enhanced TD scheme (PETD) with two servers for mobile crowdsensing, comprehensively considering both privacy and practicability. BPTD is straightforwardly conducted on shared data with few user-side interactions, while achieving high efficiency. To further liberate mobile users and prevent disclosure of the intermediates, PETD incorporates a novel partial decryption-based Paillier Cryptosystem to work with secret sharing, offering enhanced privacy protection without relying on any user-side involvement. Additionally, we improve the efficiency of PETD via data packing. Security analysis shows the desired privacy goals. Compared to prior studies with the best security guarantees, our extensive experiments demonstrate a comparable and superior performance regarding different metrics.

KW - Costs

KW - Crowdsensing

KW - Cryptography

KW - data aggregation

KW - Data privacy

KW - Mobile crowdsensing

KW - Paillier Cryptosystem

KW - Privacy

KW - Reliability

KW - secret sharing

KW - Servers

KW - truth discovery

U2 - 10.1109/TNSE.2022.3151228

DO - 10.1109/TNSE.2022.3151228

M3 - Journal article

AN - SCOPUS:85124840632

VL - 9

SP - 1740

EP - 1755

JO - IEEE Transactions on Network Science and Engineering

JF - IEEE Transactions on Network Science and Engineering

SN - 2327-4697

IS - 3

ER -

ID: 309118608