Trading Location Data with Bounded Personalized Privacy Loss
This work addresses privacy concerns for individuals in data markets by allowing personalized control over privacy loss, though it is incremental as it builds on existing data trading and privacy concepts.
The paper tackles the problem of enabling individuals to trade personal location data while ensuring bounded and personalized privacy loss, proposing two arbitrage-free trading mechanisms to address budget allocation and arbitrage-freeness challenges.
As personal data have been the new oil of the digital era, there is a growing trend perceiving personal data as a commodity. Although some people are willing to trade their personal data for money, they might still expect limited privacy loss, and the maximum tolerable privacy loss varies with each individual. In this paper, we propose a framework that enables individuals to trade their personal data with bounded personalized privacy loss, which raises technical challenges in the aspects of budget allocation and arbitrage-freeness. To deal with those challenges,we propose two arbitrage-free trading mechanisms with different advantages.