Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

1-2014

Abstract

Emerging platforms such as Amazon Mechanical Turk and Google Consumer Surveys are increasingly being used by researchers and market analysts to crowdsource large-scale survey data from on-line populations at extremely low-cost. However, by participating in successive surveys, users risk being profiled and targeted, both by surveyors and by the platform itself. In this paper we propose, develop, and evaluate the design of a crowdsourcing platform, called Loki, that is privacy conscious. Our contributions are three-fold: (a) We propose Loki, a system that allows users to obfuscate their (ratings-based or multiple-choice) responses at-source based on their chosen privacy level, and gives surveyors aggregated population averages with known statistical confidence. (b) We develop a novel selection mechanism, which the platform can use to give surveyors accurate population estimates within a cost budget, while ensuring fairness in privacy loss amongst users. (c) We evaluate our scheme both off-line using a large dataset of movie ratings, and on-line via experimentation with 131 real users using a prototype implementation on mobile devices. Our work represents a first step towards incorporating privacy protection in emerging platforms for crowdsourced survey data.

Keywords

Crowdsourcing platforms, Privacy protection

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

2014 6th International Conference on Communication Systems and Networks, COMSNETS 2014, Bangalore, India, Jan 7-10

First Page

1

Last Page

8

ISBN

9781479936359

Identifier

10.1109/COMSNETS.2014.6734877

Publisher

IEEE

City or Country

Piscataway, NJ

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/COMSNETS.2014.6734877

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