Privacy and Quality Preserving Multimedia Data Aggregation for Participatory Sensing Systems

Qiu, Fudong; Wu, Fan; Chen, Guihai

doi:10.1109/tmc.2014.2352253

Cited by 67 publications

(36 citation statements)

References 51 publications

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“…SLICER integrates a data coding technique and message transfer strategies to support strong protection of participants' privacy, while maintaining high data quality [111].…”

Section: Secure and Privacy-preserving Data Reportingmentioning

confidence: 99%

A Survey on Security, Privacy, and Trust in Mobile Crowdsourcing

Feng

Yan

Zhang

et al. 2018

IEEE Internet Things J.

117

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Abstract-With the popularity of sensor-rich mobile devices (e.g., smart phones and wearable devices), Mobile Crowdsourcing (MCS) has emerged as an effective method for data collection and processing. Compared with traditional Wireless Sensor Networking (WSN), MCS holds many advantages such as mobility, scalability, cost-efficiency, and human intelligence. However, MCS still faces many challenges with regard to security, privacy and trust. This paper provides a survey of these challenges and discusses potential solutions. We analyze the characteristics of MCS, identify its security threats, and outline essential requirements on a secure, privacy-preserving and trustworthy MCS system. Further, we review existing solutions based on these requirements and compare their pros and cons. Finally, we point out open issues and propose some future research directions.

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“…SLICER integrates a data coding technique and message transfer strategies to support strong protection of participants' privacy, while maintaining high data quality [111].…”

Section: Secure and Privacy-preserving Data Reportingmentioning

confidence: 99%

A Survey on Security, Privacy, and Trust in Mobile Crowdsourcing

Feng

Yan

Zhang

et al. 2018

IEEE Internet Things J.

117

View full text Add to dashboard Cite

show abstract

“…In [11]'s work, they using erasure code technology to slice the sensing data in their SLICER framework to meet the k-anonymity requirement. They take the sensing data as entirety and then using the erasure coding technology to code the sensing data which is inspired us.…”

Section: Sensing Data Slicing Solutionmentioning

confidence: 99%

“…In our work, we use RS code for the sensing data slicing work. Different from [11]'s work, the location data will be replaced by location anonymous algorithm like before the slicing step. That because the location is one of the most sensitive data, if the accurate location can be anonymous after data sensing step, it means that if the data slice are obtained by illegible users, they cannot acquired the true location data.…”

Section: Sensing Data Slicing Solutionmentioning

confidence: 99%

PEMM: A Privacy-Aware Data Aggregation Solution for Mobile Sensing Networks

Xie

Wang

et al. 2016

Communications in Computer and Information Science

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Abstract. By more and more, privacy preservation problem is widely discussed among users and researchers. For mobile sensing network, an imperfect privacy preservation scheme will directly put participants into a dangerous situation. The better privacy protection applied, the better sensing data quality will be achieved. In this paper, we present a privacy-aware data aggregation scheme for mobile sensing networks. We considered both the smart nodes like smart-phone and dumb nodes like wearable device or GPS device. We take the location information and the sensing content into consideration separately. And this thought will make sure the sensing content will be k-anonymous and the accurate location will be protected well either. We use erasure coding technology to slice the sensing data record according to the k-anonymity rules. For the sake of efficiency and stability, we compare two coding technology in two sensing data types and give the experiment results and explanations in detail. After that, we give a social model to describe the social relation and a security data sharing protocol among the participants. The introduction of the participants' social relation may give a new way to the reputation and data trustworthy evaluation mechanism.

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“…For example, a house agency may know Bob desire to buy a house in a particular area if Bob releases tasks to collect traffic condition and noise level in the neighborhood. To preserve the privacy for both customers and mobile users, several privacy-preserving mobile crowdsensing schemes [10], [11], [12], [13] have been proposed by utilizing anonymity techniques. Nevertheless, anonymity is insufficient for privacy preservation, since the mobile users may be traced from travel routes and social relations.…”

Section: Introductionmentioning

confidence: 99%

Enabling Strong Privacy Preservation and Accurate Task Allocation for Mobile Crowdsensing

Zhang

Xia

et al. 2020

IEEE Trans. on Mobile Comput.

153

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Mobile crowdsensing engages a crowd of individuals to use their mobile devices to cooperatively collect data about social events and phenomena for special interest customers. It can reduce the cost on sensor deployment and improve data quality with human intelligence. To enhance data trustworthiness, it is critical for service provider to recruit mobile users based on their personal features, e.g., mobility pattern and reputation, but it leads to the privacy leakage of mobile users. Therefore, how to resolve the contradiction between user privacy and task allocation is challenging in mobile crowdsensing. In this paper, we propose SPOON, a strong privacy-preserving mobile crowdsensing scheme supporting accurate task allocation from geographic information and credit points of mobile users. In SPOON, the service provider enables to recruit mobile users based on their locations, and select proper sensing reports according to their trust levels without invading user privacy. By utilizing proxy reencryption and BBS+ signature, sensing tasks are protected and reports are anonymized to prevent privacy leakage. In addition, a privacy-preserving credit management mechanism is introduced to achieve decentralized trust management and secure credit proof for mobile users. Finally, we show the security properties of SPOON and demonstrate its efficiency on computation and communication.

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Privacy and Quality Preserving Multimedia Data Aggregation for Participatory Sensing Systems

Cited by 67 publications

References 51 publications

A Survey on Security, Privacy, and Trust in Mobile Crowdsourcing

A Survey on Security, Privacy, and Trust in Mobile Crowdsourcing

PEMM: A Privacy-Aware Data Aggregation Solution for Mobile Sensing Networks

Enabling Strong Privacy Preservation and Accurate Task Allocation for Mobile Crowdsensing

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