Collective Privacy Recovery: Data-sharing Coordination via Decentralized Artificial Intelligence
This addresses privacy concerns for individuals and service providers, offering a novel approach to data-sharing coordination.
The paper tackles the problem of collective privacy loss by proposing that individuals coordinate to share minimal data for online services, and shows through a living-lab experiment with over 27,000 data disclosures that this coordination leads to significant privacy recovery and cost reductions.
Collective privacy loss becomes a colossal problem, an emergency for personal freedoms and democracy. But, are we prepared to handle personal data as scarce resource and collectively share data under the doctrine: as little as possible, as much as necessary? We hypothesize a significant privacy recovery if a population of individuals, the data collective, coordinates to share minimum data for running online services with the required quality. Here we show how to automate and scale-up complex collective arrangements for privacy recovery using decentralized artificial intelligence. For this, we compare for first time attitudinal, intrinsic, rewarded and coordinated data sharing in a rigorous living-lab experiment of high realism involving >27,000 real data disclosures. Using causal inference and cluster analysis, we differentiate criteria predicting privacy and five key data-sharing behaviors. Strikingly, data-sharing coordination proves to be a win-win for all: remarkable privacy recovery for people with evident costs reduction for service providers.