A privacy-preserving collaborative reputation system for mobile crowdsensing
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...
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Format: | Article |
Language: | English |
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Wiley
2018-09-01
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Series: | International Journal of Distributed Sensor Networks |
Online Access: | https://doi.org/10.1177/1550147718802189 |
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author | Bayan Hashr Alamri Muhammad Mostafa Monowar Suhair Alshehri |
author_facet | Bayan Hashr Alamri Muhammad Mostafa Monowar Suhair Alshehri |
author_sort | Bayan Hashr Alamri |
collection | DOAJ |
description | 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). |
format | Article |
id | doaj-art-c49a2e02439540d3a3552d22c331dee7 |
institution | Kabale University |
issn | 1550-1477 |
language | English |
publishDate | 2018-09-01 |
publisher | Wiley |
record_format | Article |
series | International Journal of Distributed Sensor Networks |
spelling | doaj-art-c49a2e02439540d3a3552d22c331dee72025-02-03T06:43:09ZengWileyInternational Journal of Distributed Sensor Networks1550-14772018-09-011410.1177/1550147718802189A privacy-preserving collaborative reputation system for mobile crowdsensingBayan Hashr AlamriMuhammad Mostafa MonowarSuhair AlshehriMobile 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).https://doi.org/10.1177/1550147718802189 |
spellingShingle | Bayan Hashr Alamri Muhammad Mostafa Monowar Suhair Alshehri A privacy-preserving collaborative reputation system for mobile crowdsensing International Journal of Distributed Sensor Networks |
title | A privacy-preserving collaborative reputation system for mobile crowdsensing |
title_full | A privacy-preserving collaborative reputation system for mobile crowdsensing |
title_fullStr | A privacy-preserving collaborative reputation system for mobile crowdsensing |
title_full_unstemmed | A privacy-preserving collaborative reputation system for mobile crowdsensing |
title_short | A privacy-preserving collaborative reputation system for mobile crowdsensing |
title_sort | privacy preserving collaborative reputation system for mobile crowdsensing |
url | https://doi.org/10.1177/1550147718802189 |
work_keys_str_mv | AT bayanhashralamri aprivacypreservingcollaborativereputationsystemformobilecrowdsensing AT muhammadmostafamonowar aprivacypreservingcollaborativereputationsystemformobilecrowdsensing AT suhairalshehri aprivacypreservingcollaborativereputationsystemformobilecrowdsensing AT bayanhashralamri privacypreservingcollaborativereputationsystemformobilecrowdsensing AT muhammadmostafamonowar privacypreservingcollaborativereputationsystemformobilecrowdsensing AT suhairalshehri privacypreservingcollaborativereputationsystemformobilecrowdsensing |