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.

翻译：集体隐私损失已成为一个巨大问题，对个人自由与民主构成紧急威胁。然而，我们是否准备好将个人数据视为稀缺资源，并秉持"尽可能少、必要时充分"的原则进行集体数据共享？我们假设，如果个体群体（即数据集体）能够协调共享运行在线服务所需的最低数据量，同时保持所需服务质量，则隐私将得到显著恢复。本文展示了如何利用去中心化人工智能实现复杂集体安排的自动化与规模化，以恢复隐私。为此，我们首次在高度真实、包含超过27,000次实际数据披露的严格生活实验室实验中，比较了态度型、内在驱动型、奖励型与协调型数据共享行为。通过因果推断与聚类分析，我们区分了预测隐私保护行为的关键标准与五种核心数据共享行为模式。引人注目的是，数据共享协调被证明是双赢解决方案：显著恢复个人隐私，同时明显降低服务提供商的成本。