Privacy-preserving QoI-aware participant coordination for mobile crowdsourcing

Zhang, Bo; Liu, Chi Harold; Lu, Jian‐yu; Song, Zheng; Ren, Ziyu; Ma, Jian; Wang, Wendong

doi:10.1016/j.comnet.2015.12.022

Cited by 64 publications

(44 citation statements)

References 22 publications

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“…In the process of popularization of Internet of things technology, the problem of privacy protection has encountered a lot of new situations and challenges. In recent years, many scholars began to study it to solve the privacy issues, and have achieved some results [3]- [7].…”

Section: Introductionmentioning

confidence: 99%

Privacy-Preserving Model of IoT Based Trust Evaluation

Chen

Tian

2017

IEICE Trans. Inf. & Syst.

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SUMMARYWith the popularization of Internet of things (IoT), the interaction between human and IoT has become a daily life. In this interaction, the objects of IoT usually require access to personal data, which are often sensitive. We propose a lightweight privacy-preserving model based on the trust evaluation that it can effectively protect privacy based on simple threshold detection. The key issue we address in this work is how to construct trust model so that non trusted objects were prevented from accessing private data. This work can be considered as a lightweight approach to access control for privacy-preservation. The main algorithm in the proposed model is a kind of dynamic self-adjusting trust evaluation mechanism that uses a combination of interaction information occurs between the human and the Internet of things, between the human and the human. According to the given threshold, the trust model can determine the data level of object access in the IoT. We have implemented a prototype of the proposed scheme, thereby demonstrating the feasibility of the proposed scheme on resource-constrained devices.

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Section: Introductionmentioning

confidence: 99%

Privacy-Preserving Model of IoT Based Trust Evaluation

Chen

Tian

2017

IEICE Trans. Inf. & Syst.

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“…Most SC studies assume the push mode and thus emphasize privacy protection during the tasking phase. With the pull mode, the main focus of privacy protection is shifted to the reporting phase, which has been well studied in the context of participatory sensing (e.g., [29,16,36,2,39]). With participatory sensing, the goal is to exploit the ability of mobile users to collect and share data using their sensor-equipped phones for a given campaign.…”

Section: Task Assignment: the Focus Of Spatial Crowdsourcingmentioning

confidence: 99%

Location Privacy in Spatial Crowdsourcing

Shahabi

2018

Handbook of Mobile Data Privacy

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Spatial crowdsourcing (SC) is a new platform that engages individuals in collecting and analyzing environmental, social and other spatiotemporal information. With SC, requesters outsource their spatiotemporal tasks (tasks associated with location and time) to a set of workers, who will perform the tasks by physically traveling to the tasks' locations. However, current solutions require the workers, who in many cases are simply volunteering for a cause, to disclose their locations to untrustworthy entities. Revealing an individual's location data to other entities may prevent people from contributing to SC applications, thus rendering location privacy a critical obstacle to the growth of SC applications. This chapter first identifies privacy threats toward both workers and requesters during the two main phases of spatial crowdsourcing, tasking and reporting. Tasking is the process of identifying which tasks should be assigned to which workers. This process is handled by a spatial crowdsourcing server (SC-server). The latter phase is reporting, in which workers travel to the tasks' locations, complete the tasks and upload their reports to the SC-server. The challenge is to enable effective and efficient tasking as well as reporting in SC without disclosing the actual locations of workers (at least until they agree to perform a task) and the tasks themselves (at least to workers who are not assigned to those tasks). This chapter aims to provide an overview of the state-of-the-art in protecting users' location privacy in spatial crowdsourcing. We provide a comparative study of a diverse set of solutions in terms of task publishing modes (push vs. pull), problem focuses (tasking and reporting), threats (server, requester and worker), and underlying technical approaches (from pseudonymity, cloaking, and perturbation to exchangebased and encryption-based techniques). The strengths and drawbacks of the techniques are highlighted, leading to a discussion of open problems and future work.

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“…Therefore, a communication mechanism is required to provide the ubiquitous and pervasive availability of data collected by IoT devices of one system to the IoT devices of other systems within the building [2,4,18]. Other important concerns for IoT in a smart building are energy efficiency [19,20], admission control [21], naming and addressing [22], privacy and security [23], mobile sensing [24,25,26], etc.…”

Section: Introductionmentioning

confidence: 99%

Device Centric Throughput and QoS Optimization for IoTsin a Smart Building Using CRN-Techniques

Aslam

Hasan

Shahid

et al. 2016

Sensors

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The Internet of Things (IoT) has gained an incredible importance in the communication and networking industry due to its innovative solutions and advantages in diverse domains. The IoT’ network is a network of smart physical objects: devices, vehicles, buildings, etc. The IoT has a number of applications ranging from smart home, smart surveillance to smart healthcare systems. Since IoT consists of various heterogeneous devices that exhibit different traffic patterns and expect different quality of service (QoS) in terms of data rate, bit error rate and the stability index of the channel, therefore, in this paper, we formulated an optimization problem to assign channels to heterogeneous IoT devices within a smart building for the provisioning of their desired QoS. To solve this problem, a novel particle swarm optimization-based algorithm is proposed. Then, exhaustive simulations are carried out to evaluate the performance of the proposed algorithm. Simulation results demonstrate the supremacy of our proposed algorithm over the existing ones in terms of throughput, bit error rate and the stability index of the channel.

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Privacy-preserving QoI-aware participant coordination for mobile crowdsourcing

Cited by 64 publications

References 22 publications

Privacy-Preserving Model of IoT Based Trust Evaluation

Privacy-Preserving Model of IoT Based Trust Evaluation

Location Privacy in Spatial Crowdsourcing

Device Centric Throughput and QoS Optimization for IoTsin a Smart Building Using CRN-Techniques

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