Abstract
With the fast proliferation of device sensing and computing, crowed sensing has become the building block of the Internet of things. Consequently, various data collection and incentive mechanisms are investigated for people-centric services. In this paper, we have investigated the problem of privacy-aware people-centric IoT service based on a tailored auction approach. We applied a bi-tier differential privacy methodology on the data collected from crowdsensing IoT devices. A corresponding pricing scheme is also proposed to ensure the property of incentive compatibility, precise service data, and anonymized query results. Comparing to traditional privacy-aware auction schemes which only focus on the cost, our corresponding precise privacy-aware auction scheme provides a tailored IoT service based on the customers' request. The proposed trial query technique is able to provide a precise assessment of service quality, thus improves the efficiency of the people-centric IoT service. The customer could enjoy the convenience of service evaluation before making a bid, while the actual service data is anonymized to guarantee the service providers' interests. We evaluate the proposed bi-tier differential privacy schema for auction-based service by conducting extensive simulations. The experimental results show that our proposed method yields higher data utility and accuracy for the IoT service customers with privacy concerns.
Highlights
Crowd sensing techniques emerge as a powerful solution in Internet of Things [1] The large amount of sensed data is aggregated and analyzed by the IoT service provider and people-centric services are provided for the customers to subscribe [2]
The associate editor coordinating the review of this manuscript and approving it for publication was Yang Xiao
Besides privacy-aware data collection, an incentive mechanism with service evaluation and the privacy protection of the service data are crucial to the quality of people-centric services
Summary
Crowd sensing techniques emerge as a powerful solution in Internet of Things [1] The large amount of sensed data is aggregated and analyzed by the IoT service provider and people-centric services are provided for the customers to subscribe [2]. Y. Tian et al.: Bi-Tier Differential Privacy for Precise Auction-Based People-Centric IoT Service. Besides privacy-aware data collection, an incentive mechanism with service evaluation and the privacy protection of the service data are crucial to the quality of people-centric services. We investigate the issue of privacy-aware people-centric IoT service subscription, and aim to provide a tailored auction mechanism for both customers and service providers with the properties of incentive compatibility, precise service data, and anonymized query results. The main difference between local differential privacy and proposed bi-tier differential privacy is the improved flexibility for the service provider and customer Both customers and service providers are able to control the cost for privacy protection based on their own assessment of the value of data. Since the owners of IoT device adopt privacy protection technique, the data collected by service providers have different anonymization levels. BI-TIER DIFFERENTIAL PRIVACY we first explain the definition of existing differential privacy technique, and propose a bi-tier differential privacy solution
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