Privacy-Preserving Incentive Mechanisms for Mobile Crowdsensing

2018 ◽  
Vol 17 (3) ◽  
pp. 47-57 ◽  
Author(s):  
Xinglin Zhang ◽  
Lingyu Liang ◽  
Chengwen Luo ◽  
Long Cheng
Keyword(s):  
Privacy Preserving ◽  
Incentive Mechanisms ◽  
Mobile Crowdsensing

scholarly journals Frameworks for Privacy-Preserving Mobile Crowdsensing Incentive Mechanisms

2018 ◽  
Vol 17 (8) ◽  
pp. 1851-1864 ◽  
Author(s):  
Jian Lin ◽  
Dejun Yang ◽  
Ming Li ◽  
Jia Xu ◽  
Guoliang Xue
Keyword(s):  
Privacy Preserving ◽  
Incentive Mechanisms ◽  
Mobile Crowdsensing

scholarly journals PARS: Privacy-Aware Reward System for Mobile Crowdsensing Systems

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7045
Author(s):  
Zhong Zhang ◽  
Dae Hyun Yum ◽  
Minho Shin
Keyword(s):  
Reward System ◽  
Privacy Preserving ◽  
Blind Signature ◽  
Sensor Data ◽  
Wide Area ◽  
Sensitive Data ◽  
Incentive Mechanisms ◽  
Mobile Crowdsensing

Crowdsensing systems have been developed for wide-area sensing tasks because humancarried smartphones are prevailing and becoming capable. To encourage more people to participate in sensing tasks, various incentive mechanisms were proposed. However, participating in sensing tasks and getting rewards can inherently risk the users’ privacy and discourage their participation. In particular, the rewarding process can expose the participants’ sensor data and possibly link sensitive data to their identities. In this work, we propose a privacy-preserving reward system in crowdsensing using the blind signature. The proposed scheme protects the participants’ privacy by decoupling contributions and rewarding claims. Our experiment results show that the proposed mechanism is feasible and efficient.

scholarly journals Privacy-Preserving Incentive Mechanism for Mobile Crowdsensing

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Tao Wan ◽  
Shixin Yue ◽  
Weichuan Liao
Keyword(s):  
High Performance ◽  
Incentive Mechanism ◽  
Ground Truth ◽  
Privacy Preserving ◽  
Dropping Out ◽  
Incentive Mechanisms ◽  
Mobile Crowdsensing ◽  
Assessment Protocol ◽  
Privacy Leakage ◽  
Truth Discovery

Incentive mechanisms are crucial for motivating adequate users to provide reliable data in mobile crowdsensing (MCS) systems. However, the privacy leakage of most existing incentive mechanisms leads to users unwilling to participate in sensing tasks. In this paper, we propose a privacy-preserving incentive mechanism based on truth discovery. Specifically, we use the secure truth discovery scheme to calculate ground truth and the weight of users’ data while protecting their privacy. Besides, to ensure the accuracy of the MCS results, a data eligibility assessment protocol is proposed to remove the sensing data of unreliable users before performing the truth discovery scheme. Finally, we distribute rewards to users based on their data quality. The analysis shows that our model can protect users’ privacy and prevent the malicious behavior of users and task publishers. In addition, the experimental results demonstrate that our model has high performance, reasonable reward distribution, and robustness to users dropping out.

Location Privacy-Preserving Task Recommendation with Geometric Range Query in Mobile Crowdsensing

2021 ◽  
pp. 1-1
Author(s):  
Chuan Zhang ◽  
Liehuang Zhu ◽  
Chang Xu ◽  
Jianbing Ni ◽  
Cheng Huang ◽  
...  
Keyword(s):  
Location Privacy ◽  
Range Query ◽  
Privacy Preserving ◽  
Mobile Crowdsensing

Accurate and Privacy-Preserving Task Allocation for Edge Computing Assisted Mobile Crowdsensing

2021 ◽  
pp. 1-14
Author(s):  
Zhihua Wang ◽  
Chaoqi Guo ◽  
Jiahao Liu ◽  
Jiamin Zhang ◽  
Yongjian Wang ◽  
...  
Keyword(s):  
Task Allocation ◽  
Privacy Preserving ◽  
Edge Computing ◽  
Mobile Crowdsensing

Design and evaluation of characteristic incentive mechanisms in Mobile Crowdsensing Systems

2015 ◽  
Vol 55 ◽  
pp. 95-106 ◽  
Author(s):  
Constantinos Marios Angelopoulos ◽  
Sotiris Nikoletseas ◽  
Theofanis P. Raptis ◽  
José Rolim
Keyword(s):  
Incentive Mechanisms ◽  
Mobile Crowdsensing

Traceable and Privacy-Preserving Non-Interactive Data Sharing in Mobile Crowdsensing

2021 ◽  
Author(s):  
Fuyuan Song ◽  
Zheng Qin ◽  
Jinwen Liang ◽  
Pulei Xiong ◽  
Xiaodong Lin
Keyword(s):  
Data Sharing ◽  
Privacy Preserving ◽  
Mobile Crowdsensing ◽  
Interactive Data

Toward Responsible AI: An Overview of Federated Learning for User-centered Privacy-preserving Computing

2021 ◽  
Vol 11 (3-4) ◽  
pp. 1-22
Author(s):  
Qiang Yang
Keyword(s):  
Private Information ◽  
High Performance ◽  
Privacy Preserving ◽  
Security And Privacy ◽  
Brute Force ◽  
New Paradigm ◽  
User Privacy ◽  
Data Governance ◽  
Incentive Mechanisms ◽  
Rule Out

With the rapid advances of Artificial Intelligence (AI) technologies and applications, an increasing concern is on the development and application of responsible AI technologies. Building AI technologies or machine-learning models often requires massive amounts of data, which may include sensitive, user private information to be collected from different sites or countries. Privacy, security, and data governance constraints rule out a brute force process in the acquisition and integration of these data. It is thus a serious challenge to protect user privacy while achieving high-performance models. This article reviews recent progress of federated learning in addressing this challenge in the context of privacy-preserving computing. Federated learning allows global AI models to be trained and used among multiple decentralized data sources with high security and privacy guarantees, as well as sound incentive mechanisms. This article presents the background, motivations, definitions, architectures, and applications of federated learning as a new paradigm for building privacy-preserving, responsible AI ecosystems.

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