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 data-sharing
choices. 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.

通过去中心化人工智能，对数据集体进行自动化协调和规模化，我们在涉及超过 27,000 个数据分享选择的严谨实验中，比较了态度、内在、奖励和协调数据分享方面的不同标准并确定了五种关键的数据分享行为。通过因果推断和聚类分析，我们证明了数据分享协调是所有人的双赢，人们可以实现明显的隐私恢复，服务提供者则可以降低成本。