Loki: A privacy-conscious platform for crowdsourced surveys

• Main Authors: KANDAPPU, Thivya, SIVARAMAN, Vijay, FRIEDMAN, Arik, BORELI, Roksana
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2014
• Subjects: Crowdsourcing platforms; Privacy protection; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/5397; https://ink.library.smu.edu.sg/context/sis_research/article/6401/viewcontent/Loki_14comsnets_av.pdf
• Institution: Singapore Management University

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.