Location privacy for crowdsourcing applications

Boutsis, Ioannis; Kalogeraki, Vana

doi:10.1145/2971648.2971741

Cited by 30 publications

(15 citation statements)

References 32 publications

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“…At a more abstract level, privacy has been identified as key property of tasks that deal with personal data, e.g., using images that show people or asking workers to share their position [Boutsis and Kalogeraki 2016]. But also information security and IP protection, that is, the protection of data and intellectual property (IP), are emerging quality attributes that affect a requester's willingness to crowdsource [Vukovic and Bartolini 2010].…”

Section: Terms and Conditionsmentioning

confidence: 99%

Quality Control in Crowdsourcing

et al. 2018

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Crowdsourcing enables one to leverage on the intelligence and wisdom of potentially large groups of individuals toward solving problems. Common problems approached with crowdsourcing are labeling images, translating or transcribing text, providing opinions or ideas, and similar -all tasks that computers are not good at or where they may even fail altogether. The introduction of humans into computations and/or everyday work, however, also poses critical, novel challenges in terms of quality control, as the crowd is typically composed of people with unknown and very diverse abilities, skills, interests, personal objectives and technological resources. This survey studies quality in the context of crowdsourcing along several dimensions, so as to define and characterize it and to understand the current state of the art. Specifically, this survey derives a quality model for crowdsourcing tasks, identifies the methods and techniques that can be used to assess the attributes of the model, and the actions and strategies that help prevent and mitigate quality problems. An analysis of how these features are supported by the state of the art further identifies open issues and informs an outlook on hot future research directions.

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Section: Terms and Conditionsmentioning

confidence: 99%

Quality Control in Crowdsourcing

et al. 2018

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“…There are also efforts concentrate on decentralized privacy preserving approaches. Boutsis and Kalogeraki proposed a privacy preserving framework PROMPT based on the theory of coresets. The PROMPT allows users to evaluate their privacy leakage on their own devices, so users can decide whether to share their data accordingly.…”

Section: Preliminariesmentioning

confidence: 99%

A differentially private method for crowdsourcing data submission

Zhang

Xiong

Ren

et al. 2018

Concurrency and Computation

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Summary In recent years, the ubiquity of mobile devices has made spatial crowdsourcing a successful business platform for conducting spatiotemporal projects. In spatial crowdsourcing, workers contribute to a project by performing a task at a specific location. However, these platforms present serious threats to people's location privacy because sensitive information may be leaked from submitted spatiotemporal data. As a result, people may be hesitant to join spatial crowdsourcing projects, which hampers further applications of this business model. In this paper, we propose a private spatial crowdsourcing data submission algorithm, called PS‐Sub. This is a differentially private method that preserves people's location privacy and provides acceptable data utility. Rigorous privacy analyses theoretically demonstrate the privacy guarantees inherent in the proposed model. Experiments based on real‐world datasets were conducted using practical evaluation metrics. The results show that our method is able to achieve location privacy preservation efficiently, at an acceptable cost for spatial crowdsourcing applications.

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“…Another focusing area is to increase anonymity of collected user location data [11], limiting shared location information, or evaluating privacy exposure level before sharing location data [12]. On the other hand, there are research works pointing out that because human mobility trace is very unique [17], even completed anatomized data can still be used to extract patterns and identify individuals [7][8][9].…”

Section: Related Workmentioning

confidence: 99%

“…To protect privacy, existing research works had focused on increasing anonymity of collected user location data [11], limiting shared location information, or evaluating privacy 2 Mobile Information Systems exposure level before sharing location data [12]. However how to protect privacy for mobile node's direct communication did not draw equal attention.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Internet Mobility and Privacy Using Public Cloud

Zhang

Durrësi

Durresi

2017

Mobile Information Systems

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Internet mobile users are concerned more and more about their privacy nowadays as both researches and real world incidents show that leaking of communication and location privacy can lead to serious consequence, and many research works have been done to anonymize individual user from aggregated location data. However, just the communication itself between the mobile users and their peers or website could collect considerable privacy of the mobile users, such as location history, to other parties. In this paper, we investigated the potential privacy risk of mobile Internet users and proposed a scalable system built on top of public cloud services that can hide mobile user's network location and traffic from communication peers. This system creates a dynamic distributed proxy network for each mobile user to minimize performance overhead and operation cost.

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Location privacy for crowdsourcing applications

Cited by 30 publications

References 32 publications

Quality Control in Crowdsourcing

Quality Control in Crowdsourcing

A differentially private method for crowdsourcing data submission

Enhanced Internet Mobility and Privacy Using Public Cloud

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