Differentially Private Location Protection for Worker Datasets in Spatial Crowdsourcing

To, Hien; Ghinita, Gabriel; Fan, Liyue; Shahabi, Cyrus

doi:10.1109/tmc.2016.2586058

Cited by 95 publications

(96 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The work in [12] proposed a new privacy preserving smart metering scheme for smart grid, which supports data aggregation, differential privacy, fault tolerance, and rangebased filtering simultaneously. To et al [13] introduced a novel privacy-aware framework for spatial crowdsourcing, which enables the participation of workers without compromising their location privacy. Focusing on the privacy protection of sensitive information in body area networks, the authors in [14,15] proposed different privacy preserving schemes, based on differential privacy model, via a tree structure and dynamic noise thresholds, respectively.…”

Section: Related Workmentioning

confidence: 99%

“…Differential privacy (DP), a privacy preserving model originated from statistical database, has currently drawn considerable attentions in research communities [10][11][12][13][14][15][16][17] due to (i) its rigorous and provable privacy guarantee and (ii) its assumption of adversaries' arbitrary background knowledge. However, DP actually assumes that the tuples in databases are independent [18].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Edge Correlation Based Differentially Private Network Data Release Method

Cai

Wang

et al. 2017

Security and Communication Networks

View full text Add to dashboard Cite

Differential privacy (DP) provides a rigorous and provable privacy guarantee and assumes adversaries' arbitrary background knowledge, which makes it distinct from prior work in privacy preserving. However, DP cannot achieve claimed privacy guarantees over datasets with correlated tuples. Aiming to protect whether two individuals have a close relationship in a correlated dataset corresponding to a weighted network, we propose a differentially private network data release method, based on edge correlation, to gain the tradeoff between privacy and utility. Specifically, we first extracted the Edge Profile (PF) of an edge from a graph, which is transformed from a raw correlated dataset. Then, edge correlation is defined based on the PFs of both edges via JensonShannon Divergence (JS-Divergence). Secondly, we transform a raw weighted dataset into an indicated dataset by adopting a weight threshold, to satisfy specific real need and decrease query sensitivity. Furthermore, we propose -correlated edge differential privacy (CEDP), by combining the correlation analysis and the correlated parameter with traditional DP. Finally, we propose network data release (NDR) algorithm based on the -CEDP model and discuss its privacy and utility. Extensive experiments over real and synthetic network datasets show the proposed releasing method provides better utilities while maintaining privacy guarantee.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Edge Correlation Based Differentially Private Network Data Release Method

Cai

Wang

et al. 2017

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…This problem is considerably more challenging when compared to the problem of privacy-preserving reporting in the pull mode. [29] x [39] x x x N/A N/A x x x Kazemi et al 2011 [16] x (x) x x (x) x x x Vu et al 2012 [36] x [33] x x x (x) (x) x x x Gong et al 2015 [9] x x x (x) (x) x x x Zhang et al 2015 [40] x x x (x) (x) x x x To et al 2016 [32] x [25] x x x (x) x x x Hu et al 2015 [12] x x x (x) x x x Shen et al 2916 [28] x…”

Section: Privacy Countermeasuresmentioning

confidence: 99%

“…Various techniques have been proposed to protect location privacy of workers during task assignment in SC, including cloaking (hide the accurate location in a cloaked region) [17,36,25,12], perturbation (distort the actual location information by adding artificial noise) [33,9,40,32] and encryption [29,28].…”

Section: Protection In the Push Modementioning

confidence: 99%

Location Privacy in Spatial Crowdsourcing

Shahabi

2018

Handbook of Mobile Data Privacy

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Crowdsourcing data are used for several types of service in open crowdsourcing platforms [27,28]. For example, real-time spatial crowdsourcing data can be used for online monitoring systems for end users, and historical spatial crowdsourcing data can be used for various types of map-based analytics services [29,30].…”

Section: Spatial Task Management Processmentioning

confidence: 99%

Task-Management Method Using R-Tree Spatial Cloaking for Large-Scale Crowdsourcing

Shin

2017

Symmetry

View full text Add to dashboard Cite

With the development of sensor technology and the popularization of the data-driven service paradigm, spatial crowdsourcing systems have become an important way of collecting map-based location data. However, large-scale task management and location privacy are important factors for participants in spatial crowdsourcing. In this paper, we propose the use of an R-tree spatial cloaking-based task-assignment method for large-scale spatial crowdsourcing. We use an estimated R-tree based on the requested crowdsourcing tasks to reduce the crowdsourcing server-side inserting cost and enable the scalability. By using Minimum Bounding Rectangle (MBR)-based spatial anonymous data without exact position data, this method preserves the location privacy of participants in a simple way. In our experiment, we showed that our proposed method is faster than the current method, and is very efficient when the scale is increased.

show abstract

Differentially Private Location Protection for Worker Datasets in Spatial Crowdsourcing

Cited by 95 publications

References 31 publications

An Edge Correlation Based Differentially Private Network Data Release Method

An Edge Correlation Based Differentially Private Network Data Release Method

Location Privacy in Spatial Crowdsourcing

Task-Management Method Using R-Tree Spatial Cloaking for Large-Scale Crowdsourcing

Contact Info

Product

Resources

About