Privacy, Altruism, and Experience: Estimating the Perceived Value of Internet Data for Medical Uses - Gefen, Gilie; Tennenholtz, Moshe; Ben-Porat, Omer (Companion Proceedings of the Web Conference 2020, 2020)
People increasingly turn to the Internet when they have a medical condition. The data they create during this process is a valuable source for medical research and future health services. When used for these purposes, it is imperative to balance use with user privacy. One way to understand how to harmonize these requirements is to match the perceived value that users assign to their data with the value of the services derived from them. Here we describe novel experiments where methods from Mechanism Design, Crowdsourcing and Data Science were used together to elicit truthful valuations from users for their Internet data and for services derived from these data, specifically for medical screening. In these experiments, 880 people from around the world were asked to participate in an auction to provide their data for uses differing in their contribution to the participant and to society, and in the disease they addressed. Some users were offered monetary compensation for their participation, while others were asked to pay to participate. Our findings show that 99% of people were willing to contribute their data in exchange for monetary compensation and an analysis of their data, while 53% were willing to pay to have their data analyzed. The average perceived value users assigned to their data was estimated at US49. Their value for services which offer personal benefit to them was US22, while the value of this service offered to the general public was US20. Our findings show that it is possible to place a monetary value on health-related uses of highly personal data. Our methodology can be extended to other areas where sensitive data may be exchanged for services to individuals and to society.