Differential Privacy-Based Location Protection in Spatial Crowdsourcing

Wei, Jianhao; Lin, Yaping; Yao, Xin; Zhang, Jin

doi:10.1109/tsc.2019.2920643

Cited by 68 publications

(27 citation statements)

References 31 publications

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“…In [135], a location privacy-preserving method leveraging spatio-temporal events of mobile users in continuous location-based services, e.g., office visitation, is investigated. Specifically, an -differential privacy is designed to protect spatio-temporal events against attackers by adding random noise to the event data [138]- [140]. In [141], the authors present a location privacy protection mechanism using data perturbation for smart health systems in hospitals.…”

Section: ) Location Information Protectionmentioning

confidence: 99%

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part II: Emerging Technologies and Open Issues

et al. 2020

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This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In Part I [1], an extensive background of social distancing is provided, and enabling wireless technologies are thoroughly surveyed. In this Part II, emerging technologies such as machine learning, computer vision, thermal, ultrasound, etc., are introduced. These technologies open many new solutions and directions to deal with problems in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. Finally, we discuss open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice. As an example, instead of reacting with ad-hoc responses to COVID-19-like pandemics in the future, smart infrastructures (e.g., next-generation wireless systems like 6G, smart home/building, smart city, intelligent transportation systems) should incorporate a pandemic mode in their standard architectures/designs.

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Section: ) Location Information Protectionmentioning

confidence: 99%

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part II: Emerging Technologies and Open Issues

et al. 2020

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“…An adversary can potentially infer the user's location from tracking the accepted tasks, by linking the task's location to the user's. Therefore, privacy method presented in [47] splits the original locations of both tasks and workers in the crowdsourcing application into three-level grids to better represent sparse and dense areas.…”

Section: Location Aggregate Sharingmentioning

confidence: 99%

“…Location data analysis Aggregation Grid partitioning [44], [45], [46], and [47] Divide the location space using a uniform grid, then induce noise to the counts of each cell (C1) Utility of range queries may be compromised due to the accumulation of noise from multiple cells/regions and the difference in density between regions .…”

Section: Challengesmentioning

confidence: 99%

An Analysis of Differential Privacy Research in Location and Trajectory Data

Errounda

Liu²

2020

Preprint

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Location and trajectory data are routinely collected to generate valuable knowledge about users' pattern behavior. However, releasing location data may jeopardize the privacy of the involved individuals. Differential privacy is a powerful technique that prevents an adversary from inferring the presence or absence of an individual in the original data solely based on the observed data. The first challenge in applying differential privacy in location is that a it usually involves a single user. This shifts the adversary's target to the user's locations instead of presence or absence in the original data. The second challenge is that the inherent correlation between location data, due to people's movement regularity and predictability, gives the adversary an advantage in inferring information about individuals. In this paper, we review the differentially private approaches to tackle these challenges. Our goal is to help newcomers to the field to better understand the state-of-the art by providing a research map that highlights the different challenges in designing differentially private frameworks that tackle the characteristics of location data. We find that in protecting an individual's location privacy, the attention of differential privacy mechanisms shifts to preventing the adversary from inferring the original location based on the observed one. Moreover, we find that the privacy-preserving mechanisms make use of the predictability and regularity of users' movements to design and protect the users' privacy in trajectory data. Finally, we explore how well the presented frameworks succeed in protecting users' locations and trajectories against well-known privacy attacks.

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“…Location privacy has received wide attention on MCS in recent years. There are many ways to protect location privacy, and we mainly introduce and compare spatial cloaking, 25,29,53 encryption, 54‐57 and perturbation 32,33,35,36,58 …”

Section: Related Workmentioning

confidence: 99%

Repot: Real‐time and privacy‐preserving online task assignment for mobile crowdsensing

Xia

Zhao

Tang

et al. 2020

Trans Emerging Tel Tech

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Recently, the development of Internet of Things and mobile sensor technology has facilitated mobile crowdsensing (MCS) as a popular data collection paradigm. In MCS, requesters publish tasks to the sensing platform, and then the sensing platform assigns tasks to workers who carry mobile sensing devices. In general, the sensing platform matches tasks and workers according to the location and time attributes of tasks and workers. However, most current studies rarely consider location and time attributes, simultaneously. In previous work, it is difficult to achieve real‐time task assignment while protecting worker's location privacy. To tackle the above problem, in this article, we propose a real‐time and privacy‐preserving online task assignment scheme, named Repot. In Repot, the location privacy of workers is protected by using geoindistinguishability. And we design a probabilistic method to quantify the reachability between the obfuscated worker and the task. In addition, the worker‐based distance comparison mechanism and the task‐based distance comparison mechanism are designed to reduce the overall distance of workers in Repot, respectively. The experimental evaluation is performed on a real‐world data set and the results show the feasibility and effectiveness of Repot.

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Differential Privacy-Based Location Protection in Spatial Crowdsourcing

Cited by 68 publications

References 31 publications

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part II: Emerging Technologies and Open Issues

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part II: Emerging Technologies and Open Issues

An Analysis of Differential Privacy Research in Location and Trajectory Data

Repot: Real‐time and privacy‐preserving online task assignment for mobile crowdsensing

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