Evidence map›Paper›PMID 34770352›Full record

ArticleSensors (Basel, Switzerland)2021

PARS: Privacy-Aware Reward System for Mobile Crowdsensing Systems.

Zhong Zhang, Dae Hyun Yum, Minho Shin

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-weighted citation impact, top 35% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 4 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Zhong ZhangDepartment of Computer Engineering, Myongji University, Yongin 17058, Korea.
Dae Hyun YumDepartment of Information and Communication Engineering, Myongji University, Yongin 17058, Korea.
Minho ShinDepartment of Computer Engineering, Myongji University, Yongin 17058, Korea.
Myongji University · KR

Funding

National Research Foundation of Korea 2021R1F1A1055324
6 · The paper itself

Abstract

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.

Indexed as

PrivacySmartphoneHumansRewardaggregationblind signatureincentivemobile crowdsensingprivacy-preserving

Identifiers

PMID34770352
PMCPMC8586992
OpenAlexW3209406510

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.