Sell your experiences: a market mechanism based incentive for participatory sensing

Lee, Juong-Sik; Hoh, Baik

doi:10.1109/percom.2010.5466993

Cited by 345 publications

(207 citation statements)

References 7 publications

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“…Hence, there are conflicting goals between the user and the check-in collector: the former would like to choose large values of L to have strong location privacy, while the latter would prefer small values to retain more check-in data. In order to address this issue, reward mechanisms should be adopted to incentivize personal data sharing, like proposed in recent works (e.g., [14]). …”

Section: Discussionmentioning

confidence: 99%

Differentially-private release of check-in data for venue recommendation

Riboni

Bettini

2014

2014 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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Abstract-Recommender systems suggesting venues offer very useful services to people on the move and a great business opportunity for advertisers. These systems suggest venues by matching the current context of the user with the venue features, and consider the popularity of venues, based on the number of visits ("check-ins") that they received. Check-ins may be explicitly communicated by users to geo-social networks, or implicitly derived by analysing location data collected by mobile services. In general, the visibility of explicit check-ins is limited to friends in the social network, while the visibility of implicit check-ins limited to the service provider. Exposing check-ins to unauthorized users is a privacy threat since recurring presence in given locations may reveal political opinions, religious beliefs, or sexual orientation, as well as absence from other locations where the user is supposed to be. Hence, on one side mobile app providers host valuable information that recommender system providers would like to buy and use to improve their systems, and on the other we recognize serious privacy issues in releasing that information. In this paper, we solve this dilemma by providing formal privacy guarantees to users and trusted mobile providers while preserving the utility of check-in information for recommendation purposes. Our technique is based on the use of differential privacy methods integrated with a pre-filtering process, and protects against both an untrusted recommender system and its users, willing to infer the venues and sensitive locations visited by other users. Extensive experiments with a large dataset of real users' check-ins show the effectiveness of our methods.

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Section: Discussionmentioning

confidence: 99%

Differentially-private release of check-in data for venue recommendation

Riboni

Bettini

2014

2014 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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“…Recently, numerous game theory approaches [39][40][41] have been proposed for mobile crowdsensing and computing to encourage and reward truthful contributions. These game theory techniques are usually based on auction mechanisms, however slightly complex to apply in a fully distributed and time evolving system.…”

Section: Crowdsensing Challengesmentioning

confidence: 99%

Crowdsensing: Socio-Technical Challenges and Opportunities

Noureen¹,

Asif²

2017

ijacsa

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“…[11][12][13][14][15][16]) have been proposed in the literature, but not many of them have been applied in real-world crowdsensing applications. Viewed with the lens of SPECTRUM, Table 1 shows how a combination of factors could influence the choice of incentive used in real-world mobile crowdsensing applications.…”

Section: Adoption Of Incentive Mechanisms In Real-world Mobile Crowdsmentioning

confidence: 99%

“…[11][12][13][14][15][16]) have been proposed to address this problem, many of which are designed to suit the crowdsensing context and specific requirements of the underlying sensing initiative. Despite the proliferation of these technical solutions, Restuccia et al [17] draw attention to the fact that it is yet unclear what is the most appropriate incentive to adopt in motivating users to participate in mobile crowdsensing.…”

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confidence: 99%

Adopting incentive mechanisms for large-scale participation in mobile crowdsensing: from literature review to a conceptual framework

Ogie

2016

Hum. Cent. Comput. Inf. Sci.

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Mobile crowdsensing is a burgeoning concept that allows smart cities to leverage the sensing power and ubiquitous nature of mobile devices in order to capture and map phenomena of common interest. At the core of any successful mobile crowdsensing application is active user participation, without which the system is of no value in sensing the phenomenon of interest. A major challenge militating against widespread use and adoption of mobile crowdsensing applications is the issue of how to identify the most appropriate incentive mechanism for adequately and efficiently motivating participants. This paper reviews literature on incentive mechanisms for mobile crowdsensing and proposes the concept of SPECTRUM as a guide for inferring the most appropriate type of incentive suited to any given crowdsensing task. Furthermore, the paper highlights research challenges and areas where additional studies related to the different factors outlined in the concept of SPECTRUM are needed to improve citizen participation in mobile crowdsensing. It is envisaged that the broad range of factors covered in SPECTRUM will enable smart cities to efficiently engage citizens in large-scale crowdsensing initiatives. More importantly, the paper is expected to trigger empirical investigations into how various factors as outlined in SPECTRUM can influence the type of incentive mechanism that is considered most appropriate for any given mobile crowdsensing initiative.

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Sell your experiences: a market mechanism based incentive for participatory sensing

Cited by 345 publications

References 7 publications

Differentially-private release of check-in data for venue recommendation

Differentially-private release of check-in data for venue recommendation

Crowdsensing: Socio-Technical Challenges and Opportunities

Adopting incentive mechanisms for large-scale participation in mobile crowdsensing: from literature review to a conceptual framework

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