PARS: Privacy-Aware Reward System for Mobile Crowdsensing Systems

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.

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Frameworks for Privacy-Preserving Mobile Crowdsensing Incentive Mechanisms

IEEE Transactions on Mobile Computing ◽

10.1109/tmc.2017.2780091 ◽

2018 ◽

Vol 17 (8) ◽

pp. 1851-1864 ◽

Cited By ~ 30

Author(s):

Jian Lin ◽

Dejun Yang ◽

Ming Li ◽

Jia Xu ◽

Guoliang Xue

Keyword(s):

Privacy Preserving ◽

Incentive Mechanisms ◽

Mobile Crowdsensing

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Privacy-Preserving Incentive Mechanisms for Mobile Crowdsensing

IEEE Pervasive Computing ◽

10.1109/mprv.2018.03367734 ◽

2018 ◽

Vol 17 (3) ◽

pp. 47-57 ◽

Cited By ~ 7

Author(s):

Xinglin Zhang ◽

Lingyu Liang ◽

Chengwen Luo ◽

Long Cheng

Keyword(s):

Privacy Preserving ◽

Incentive Mechanisms ◽

Mobile Crowdsensing

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Privacy-Preserving Incentive Mechanism for Mobile Crowdsensing

Security and Communication Networks ◽

10.1155/2021/4804758 ◽

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.

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Detection of Sensitive Data Leakage for Privacy Preserving

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v6i9.765769 ◽

2018 ◽

Vol 6 (9) ◽

pp. 765-769

Author(s):

R.J. Patil ◽

Y.S. Borse

Keyword(s):

Privacy Preserving ◽

Sensitive Data

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Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study

npj Digital Medicine ◽

10.1038/s41746-021-00431-6 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Qi Dou ◽

Tiffany Y. So ◽

Meirui Jiang ◽

Quande Liu ◽

Varut Vardhanabhuti ◽

...

Keyword(s):

Data Privacy ◽

Medical Training ◽

External Validation ◽

Mainland China ◽

Privacy Preserving ◽

Generalization Capability ◽

Major Focus ◽

Sensitive Data ◽

Lung Abnormalities ◽

Medical Image Diagnosis

AbstractData privacy mechanisms are essential for rapidly scaling medical training databases to capture the heterogeneity of patient data distributions toward robust and generalizable machine learning systems. In the current COVID-19 pandemic, a major focus of artificial intelligence (AI) is interpreting chest CT, which can be readily used in the assessment and management of the disease. This paper demonstrates the feasibility of a federated learning method for detecting COVID-19 related CT abnormalities with external validation on patients from a multinational study. We recruited 132 patients from seven multinational different centers, with three internal hospitals from Hong Kong for training and testing, and four external, independent datasets from Mainland China and Germany, for validating model generalizability. We also conducted case studies on longitudinal scans for automated estimation of lesion burden for hospitalized COVID-19 patients. We explore the federated learning algorithms to develop a privacy-preserving AI model for COVID-19 medical image diagnosis with good generalization capability on unseen multinational datasets. Federated learning could provide an effective mechanism during pandemics to rapidly develop clinically useful AI across institutions and countries overcoming the burden of central aggregation of large amounts of sensitive data.

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

IEEE Transactions on Mobile Computing ◽

10.1109/tmc.2021.3080714 ◽

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

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Privacy-Preserving Deep Neural Network Methods: Computational and Perceptual Methods—An Overview

Electronics ◽

10.3390/electronics10111367 ◽

2021 ◽

Vol 10 (11) ◽

pp. 1367

Author(s):

Raghida El El Saj ◽

Ehsan Sedgh Sedgh Gooya ◽

Ayman Alfalou ◽

Mohamad Khalil

Keyword(s):

Neural Networks ◽

Classification Accuracy ◽

Deep Neural Networks ◽

Privacy Preserving ◽

Sensitive Data ◽

Encrypted Data ◽

High Classification Accuracy ◽

Hide Information ◽

Network Methods ◽

Cloud Environments

Privacy-preserving deep neural networks have become essential and have attracted the attention of many researchers due to the need to maintain the privacy and the confidentiality of personal and sensitive data. The importance of privacy-preserving networks has increased with the widespread use of neural networks as a service in unsecured cloud environments. Different methods have been proposed and developed to solve the privacy-preserving problem using deep neural networks on encrypted data. In this article, we reviewed some of the most relevant and well-known computational and perceptual image encryption methods. These methods as well as their results have been presented, compared, and the conditions of their use, the durability and robustness of some of them against attacks, have been discussed. Some of the mentioned methods have demonstrated an ability to hide information and make it difficult for adversaries to retrieve it while maintaining high classification accuracy. Based on the obtained results, it was suggested to develop and use some of the cited privacy-preserving methods in applications other than classification.

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