PARE:Privacy-Preserving Data Reliability Evaluation for Spatial Crowdsourcing in Internet of Things

The proliferation of intelligent,connected Internet of Things(IoT)devices facilitates data collection.However,task workers may be reluctant to participate in data collection due to privacy concerns,and task requesters may be concerned about the validity of the collected data.Hence,it is vital to evaluate the quality of the data collected by the task workers while protecting privacy in spatial crowdsourcing(SC)data collection tasks with IoT.To this end,this paper proposes a privacy-preserving data reliability evaluation for SC in IoT,named PARE.First,we design a data uploading format using blockchain and Paillier homomorphic cryptosystem,providing unchangeable and traceable data while overcoming privacy concerns.Secondly,based on the uploaded data,we propose a method to determine the approximate correct value region without knowing the exact value.Finally,we offer a data filtering mechanism based on the Paillier cryptosystem using this value region.The evaluation and analysis results show that PARE outperforms the existing solution in terms of performance and privacy protection.

Transformer-Aided Deep Double Dueling Spatial-Temporal Q-Network for Spatial Crowdsourcing Analysis

With the rapid development ofmobile Internet,spatial crowdsourcing has becomemore andmore popular.Spatial crowdsourcing consists of many different types of applications,such as spatial crowd-sensing services.In terms of spatial crowd-sensing,it collects and analyzes traffic sensing data from clients like vehicles and traffic lights to construct intelligent traffic prediction models.Besides collecting sensing data,spatial crowdsourcing also includes spatial delivery services like DiDi and Uber.Appropriate task assignment and worker selection dominate the service quality for spatial crowdsourcing applications.Previous research conducted task assignments via traditional matching approaches or using simple network models.However,advanced mining methods are lacking to explore the relationship between workers,task publishers,and the spatio-temporal attributes in tasks.Therefore,in this paper,we propose a Deep Double Dueling Spatial-temporal Q Network(D3SQN)to adaptively learn the spatialtemporal relationship between task,task publishers,and workers in a dynamic environment to achieve optimal allocation.Specifically,D3SQNis revised through reinforcement learning by adding a spatial-temporal transformer that can estimate the expected state values and action advantages so as to improve the accuracy of task assignments.Extensive experiments are conducted over real data collected fromDiDi and ELM,and the simulation results verify the effectiveness of our proposed models.

Privacy-Preserving Task Assignment in Spatial Crowdsourcing

With the progress of mobile devices and wireless networks, spatial crowdsourcing （SC） is emerging as a promising approach for problem solving. In SC, spatial tasks are assigned to and performed by a set of human workers. To enable effective task assignment, however, both workers and task requesters are required to disclose their locations to untrusted SC systems. In this paper, we study the problem of assigning workers to tasks in a way that location privacy for both workers and task requesters is preserved. We first combine the Paillier cryptosystem with Yao＆#39;s garbled circuits to construct a secure protocol that assigns the nearest worker to a task. Considering that this protocol cannot scale to a large number of workers, we then make use of Geohash, a hierarchical spatial index to design a more efficient protocol that can securely find approximate nearest workers. We theoretically show that these two protocols are secure against semi-honest adversaries. Through extensive experiments on two real-world datasets, we demonstrate the efficiency and effectiveness of our protocols.

Privacy-friendly spatial crowdsourcing in vehic-ular networks

With the evolution of conventional VANETs(Vehicle Ad-hoc Networks)into the IoV(Internet of Vehicles),vehicle-based spatial crowdsourcing has become a potential solution for crowdsourcing applications.In vehicular networks,a spatial-temporal task/question can be outsourced(i.e.,task/question relating to a particular location and in a speci c time period)to some suitable smart vehicles(also known as workers)and then these workers can help solve the task/question.However,an inevitable barrier to the widespread deployment of spatial crowdsourcing applications in vehicular networks is the concern of privacy.Hence,We propose a novel privacy-friendly spatial crowdsourcing scheme.Unlike the existing schemes,the proposed scheme considers the privacy issue from a new perspective according that the spatial-temporal tasks can be linked and analyzed to break the location privacy of workers.Speci cally,to address the challenge,three privacy requirements(i.e.anonymity,untraceability,and unlinkability)are de ned and the proposed scheme combines an effcient anonymous technique with a new composite privacy metric to protect against attackers.Detailed privacy analyses show that the proposed scheme is privacy-friendly.In addition,performance evaluations via extensive simulations are also conducted,and the results demonstrate the effciency and e ectiveness of the proposed scheme.

Budget-Aware Dynamic Incentive Mechanism in SpatialCrowdsourcing

The ubiquitous deployment of GPS-equipped devices and mobile networks has spurred the popularity of spatial crowdsourcing. Many spatial crowdsourcing tasks require crowd workers to collect data from different locations. Since workers tend to select locations nearby or align to their routines, data collected by workers are usually unevenly distributed across the region. To encourage workers to choose remote locations so as to avoid imbalanced data collection, we investigate the incentive mechanisms in spatial crowdsourcing. We propose a price adjustment function and two algorithms, namely DFBA (Dynamic Fixed Budget Allocation) and DABA (Dynamic Adjusted Budget Allocation), which utilize price leverage to mitigate the imbalanced data collection problem. Extensive evaluations on both synthetic and real-world datasets demonstrate that the proposed incentive mechanisms are able to effectively balance the popularity of different locations.