scholarly journals A Privacy-Preserving Incentive Mechanism for Participatory Sensing Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Xiaoguang Niu ◽  
Jiawei Wang ◽  
Qiongzan Ye ◽  
Yihao Zhang
Keyword(s):  
Dynamic Balance ◽  
Incentive Mechanism ◽  
Privacy Preserving ◽  
Circulation System ◽  
Participatory Sensing ◽  
Sensing Systems ◽  
Sensing Applications ◽  
Ideal Situation ◽  
Conflicting Objectives ◽  
Share Information

The proliferation of mobile devices has facilitated the prevalence of participatory sensing applications in which participants collect and share information in their environments. The design of a participatory sensing application confronts two challenges: “privacy” and “incentive” which are two conflicting objectives and deserve deeper attention. Inspired by physical currency circulation system, this paper introduces the notion of E-cent, an exchangeable unit bearer currency. Participants can use the E-cent to take part in tasks anonymously. By employing E-cent, we propose an E-cent-based privacy-preserving incentive mechanism, called EPPI. As a dynamic balance regulatory mechanism, EPPI can not only protect the privacy of participant, but also adjust the whole system to the ideal situation, under which the rated tasks can be finished at minimal cost. To the best of our knowledge, EPPI is the first attempt to build an incentive mechanism while maintaining the desired privacy in participatory sensing systems. Extensive simulation and analysis results show that EPPI can achieve high anonymity level and remarkable incentive effects.

scholarly journals A privacy-preserving collaborative reputation system for mobile crowdsensing

2018 ◽  
Vol 14 (9) ◽  
pp. 155014771880218 ◽  
Author(s):  
Bayan Hashr Alamri ◽  
Muhammad Mostafa Monowar ◽  
Suhair Alshehri
Keyword(s):  
Security Analysis ◽  
Privacy Preserving ◽  
Reputation System ◽  
Mobile Crowdsensing ◽  
Sensing Applications ◽  
Conflicting Objectives ◽  
Trusted Data ◽  
Privacy Issues ◽  
Open Nature

Mobile crowdsensing is an emerging technology in which participants contribute sensor readings for different sensing applications. This technology enables a broad range of sensing applications by utilizing smartphones and tablets worldwide to improve people’s quality of life. Protecting participants’ privacy and ensuring the trustworthiness of the sensor readings are conflicting objectives and key challenges in this field. Privacy issues arise from the disclosure of the participant-related context information, such as participants’ location. Trustworthiness issues arise from the open nature of sensing system because anyone can contribute data. This article proposes a privacy-preserving collaborative reputation system that preserves privacy and ensures data trustworthiness of the sensor readings for mobile crowdsensing applications. The proposed work also counters a number of possible attacks that might occur in mobile crowdsensing applications. We provide a detailed security analysis to prove the effectiveness of privacy-preserving collaborative reputation system against a number of attacks. We conduct an extensive simulation to investigate the performance of our schema. The obtained results show that the proposed schema is practical; it succeeds in identifying malicious users in most scenarios. In addition, it tolerates a large number of colluding adversaries even if their number surpass 65%. Moreover, it detects on-off attackers even if they report trusted data with high probability (0.8).

Participatory Sensing for City-Scale Applications

2017 ◽  
pp. 210-230
Author(s):  
Tridib Mukherjee ◽  
Deepthi Chander ◽  
Sharanya Eswaran ◽  
Koustuv Dasgupta
Keyword(s):  
Smart Phones ◽  
Participatory Sensing ◽  
Community Based ◽  
Scientific Paradigm ◽  
Sensing Systems ◽  
Sensing Applications ◽  
Macro Scale ◽  
Urban Sensing

The rapid advancements in sensing, computation and communications have led to the proliferation of smart phones. People-centric sensing is a scientific paradigm which empowers citizens with sensor-embedded smartphones, to contribute to micro and macro-scale urban sensing applications – either implicitly (in an opportunistic manner) or explicitly (in a participatory manner). Community-based urban sensing applications, are typically participatory in nature. For instance, commuters reporting on a transit overload may explicitly need to provide an input through an app to report on the overload. This chapter will focus on the trends, challenges and applications of participatory sensing systems. Additionally, they will understand the solution requirements for effective deployments of such systems in real scenarios.

scholarly journals Anonymous Data Reporting Strategy with Dynamic Incentive Mechanism for Participatory Sensing

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Yang Li ◽  
Hongtao Song ◽  
Yunlong Zhao ◽  
Nianmin Yao ◽  
Nianbin Wang
Keyword(s):  
Incentive Mechanism ◽  
Personal Data ◽  
Low Latency ◽  
Participatory Sensing ◽  
Data Reporting ◽  
Additional Information ◽  
Sensing Applications ◽  
Multimodal Information ◽  
Intelligent Devices ◽  
The Incentive Mechanism

Participatory sensing is often used in environmental or personal data monitoring, wherein a number of participants collect data using their mobile intelligent devices for earning the incentives. However, a lot of additional information is submitted along with the data, such as the participant’s location, IP and incentives. This multimodal information implicitly links to the participant’s identity and exposes the participant’s privacy. In order to solve the issue of these multimodal information associating with participants’ identities, this paper proposes a protocol to ensure anonymous data reporting while providing a dynamic incentive mechanism simultaneously. The proposed protocol first establishes a submission schedule by anonymously selecting a slot in a vector by each member where every member and system entities are oblivious of other members’ slots and then uses this schedule to submit the all members’ data in an encoded vector through bulk transfer and multiplayer dining cryptographers networks (DC-nets) . Hence, the link between the data and the member’s identity is broken. The incentive mechanism uses blind signature to anonymously mark the price and complete the micropayments transfer. Finally, the theoretical analysis of the protocol proves the anonymity, integrity, and efficiency of this protocol. We implemented and tested the protocol on Android phones. The experiment results show that the protocol is efficient for low latency tolerable applications, which is the cases with most participatory sensing applications, and they also show the advantage of our optimization over similar anonymous data reporting protocols.

When Crowdsourcing Meets Social IoT: An Efficient Privacy-Preserving Incentive Mechanism

2019 ◽  
Vol 6 (6) ◽  
pp. 9707-9721 ◽  
Author(s):  
Xiaoying Gan ◽  
Yuqing Li ◽  
Yixuan Huang ◽  
Luoyi Fu ◽  
Xinbing Wang
Keyword(s):  
Incentive Mechanism ◽  
Privacy Preserving
Sign in / Sign up

Export Citation Format

Share Document