  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.

Artificial Intelligence;Computers and Society;Distributed, Parallel, and Cluster Computing;Information Retrieval;Multiagent Systems