Privacy-Enhanced and Practical Truth Discovery in Two-Server Mobile Crowdsensing
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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 journal › Journal article › peer-review
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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