A privacy-preserving task recommendation framework for mobile crowdsourcing

Yanmin Gong; Yuanxiong Guo; Yuguang Fang

doi:10.1109/GLOCOM.2014.7036871

A privacy-preserving task recommendation framework for mobile crowdsourcing

Yanmin Gong, Yuanxiong Guo, Yuguang Fang

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

20 Citations (Scopus)

Abstract

Mobile crowdsourcing enables mobile workers to complete a broad range of crowdsourcing tasks anywhere at any time. However, recommending suitable crowdsourcing tasks to mobile workers requires sensitive information such as location and activity, which raises serious privacy concerns. In this paper, we formulate the task recommendation process as an optimization problem which balances privacy, utility, and efficiency. We show that this optimization problem is NP-hard, and present a greedy solution which approximates the optimal solution within a factor of 1 - 1/e. We also design an efficient aggregation protocol to compute statistics of mobile workers required in the optimization problem while providing strong privacy guarantee. Both numerical evaluations and performance analysis are carried out to show the effectiveness and efficiency of the proposed framework. To the best of our knowledge, our work is the first to consider privacy issues in task recommendation for mobile crowdsourcing.

Original language	English
Title of host publication	2014 IEEE Global Communications Conference, GLOBECOM 2014
Publisher	IEEE
Pages	588-593
ISBN (Print)	9781479935116
DOIs	https://doi.org/10.1109/GLOCOM.2014.7036871
Publication status	Published - 9 Feb 2014
Externally published	Yes
Event	2014 IEEE Global Communications Conference, GLOBECOM 2014 - Austin, United States Duration: 8 Dec 2014 → 12 Dec 2014

Conference

Conference	2014 IEEE Global Communications Conference, GLOBECOM 2014
Place	United States
City	Austin
Period	8/12/14 → 12/12/14

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Research Keywords

Mobile crowdsourcing
privacy
security
task recommendation

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abstract = "Mobile crowdsourcing enables mobile workers to complete a broad range of crowdsourcing tasks anywhere at any time. However, recommending suitable crowdsourcing tasks to mobile workers requires sensitive information such as location and activity, which raises serious privacy concerns. In this paper, we formulate the task recommendation process as an optimization problem which balances privacy, utility, and efficiency. We show that this optimization problem is NP-hard, and present a greedy solution which approximates the optimal solution within a factor of 1 - 1/e. We also design an efficient aggregation protocol to compute statistics of mobile workers required in the optimization problem while providing strong privacy guarantee. Both numerical evaluations and performance analysis are carried out to show the effectiveness and efficiency of the proposed framework. To the best of our knowledge, our work is the first to consider privacy issues in task recommendation for mobile crowdsourcing.",

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N2 - Mobile crowdsourcing enables mobile workers to complete a broad range of crowdsourcing tasks anywhere at any time. However, recommending suitable crowdsourcing tasks to mobile workers requires sensitive information such as location and activity, which raises serious privacy concerns. In this paper, we formulate the task recommendation process as an optimization problem which balances privacy, utility, and efficiency. We show that this optimization problem is NP-hard, and present a greedy solution which approximates the optimal solution within a factor of 1 - 1/e. We also design an efficient aggregation protocol to compute statistics of mobile workers required in the optimization problem while providing strong privacy guarantee. Both numerical evaluations and performance analysis are carried out to show the effectiveness and efficiency of the proposed framework. To the best of our knowledge, our work is the first to consider privacy issues in task recommendation for mobile crowdsourcing.

AB - Mobile crowdsourcing enables mobile workers to complete a broad range of crowdsourcing tasks anywhere at any time. However, recommending suitable crowdsourcing tasks to mobile workers requires sensitive information such as location and activity, which raises serious privacy concerns. In this paper, we formulate the task recommendation process as an optimization problem which balances privacy, utility, and efficiency. We show that this optimization problem is NP-hard, and present a greedy solution which approximates the optimal solution within a factor of 1 - 1/e. We also design an efficient aggregation protocol to compute statistics of mobile workers required in the optimization problem while providing strong privacy guarantee. Both numerical evaluations and performance analysis are carried out to show the effectiveness and efficiency of the proposed framework. To the best of our knowledge, our work is the first to consider privacy issues in task recommendation for mobile crowdsourcing.

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A privacy-preserving task recommendation framework for mobile crowdsourcing

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