Privacy Trading Ecosystems in the Era of Information Surveillance
[Paper] [Letter] [Demo]
Junhui Li, Yixuan Wang, Xinlong Yin
Faculty Mentors: Ranjan Pal and Mingyan Liu
Data of billions of online individuals are currently gathered, processed, and analyzed for personalized advertising or other online service. This trend is on the rise, both fueling and fueled by an increase in online apps, IoT technologies, and ad- vanced artificial intelligence (AI) and machine learning (ML) methodologies. It is a widely accepted notion in economics that sharing individual information with the demand side of an information market is beneficial to targeted customization, demand side profit, and the growth of data-‘hungry’ AI/ML controlled businesses. It has also been argued by economists that because of these benefits that individual data brings to a market, a competitive market mechanism might generate too little data sharing from the supply side.
Findings
- Private but correlated data of individuals are underpriced in general market competition resulting in diminishing economic utilitarian welfare.
- Negative externality, in the form of privacy loss to one user as a result of the disclosure of another user’s data, is very common among platform users.
- We provides regulatory insights on user information management for social networking platforms with our mathematical model.
- We pave the way for a future general theory of community data trading in n-platform oligopoly markets.
My contributions
- Utilized machine learning and Bayesian statistics methods to research the dependence of the surveyed 300,000 mobile apps users’ social backgrounds and willingness to trade privacy.
- Constructed a Bayesian network to study how financial incentives impact the extent to which users are willing to trade privacy; analyzed the underlying social patterns that characterize the preferences.
- Contributed to a second-author paper accepted by TNSM 2021, as well as one first-author letter accepted by IEEE Networking Letters 2020