RESAT: A Utility-Aware Incentive Mechanism Based Distributed Spatial Cloaking
IEEE Internet of Things Journal
; : 1-1, 2022.
Article
in English
| Scopus | ID: covidwho-1961406
ABSTRACT
Distributed Spatial Cloaking () enables users to enjoy precise Location-Based Service (LBS) with location privacy-preserving. An incentive mechanism is necessary to encourage users to cooperate. However, due to the inappropriate design of incentive mechanisms, the existing works cause low user benefits and fail to encourage users, ruining the expected incentive effect. Moreover, introducing a third party to manage users’information also causes the existing works to disclose users’privacy and be unpractical. To address these issues, we propose a utility-awaRe incEntive mechanism based diStributed spATial cloaking (RESAT). By the idea of utility theory and optimization theory, RESAT devises basic and extended incentive mechanisms. The two mechanisms for assuming that all users are honest and that malicious users provide unreasonable locations. RESAT proposes an incentive mechanism-based cloaking cooperation without a third party, incorporating the developed mechanisms based on the blind signature. Theoretical analysis indicates that RESAT achieves incentive compatibility and is secure. Extensive experiments on the real dataset show that compared with the existing works, RESAT enables 1 time more users to cooperate at best while eliminating the malicious behaviors that provide unreasonable locations. The required construction time delay is limited. IEEE
cooperation; Costs; COVID-19; Distributed spatial cloaking (); effective incentive; Internet of Things; Optimization; Privacy; Public key; Utility theory; utility-aware; Location; Location based services; Privacy-preserving techniques; Site selection; Telecommunication services; Incentive mechanism; Mechanism-based; Optimisations; Public keys
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
IEEE Internet of Things Journal
Year:
2022
Document Type:
Article
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