Collective privacy recovery: Data-sharing coordination via decentralized artificial intelligence
Collective privacy recovery: Data-sharing coordination via decentralized artificial intelligence
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DOI：
10.1093/pnasnexus/pgae029
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发表时间：
2023-01
期刊：
影响因子：
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通讯作者：
Evangelos Pournaras;M. Ballandies;S. Bennati;Chien-fei Chen
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作者：
Evangelos Pournaras;M. Ballandies;S. Bennati;Chien-fei Chen
集体隐私丧失已成为一个巨大的问题，对个人自由和民主来说是一个紧急事件。但是，我们是否准备好将个人数据作为稀缺资源来处理，并在“尽可能少，尽可能多”的原则下集体共享数据？我们假设，如果一群个人，即数据集体，协调共享最低限度的数据，以运行具有所需质量的在线服务，则可以显著恢复隐私。在这里，我们将展示如何使用分散的人工智能来自动化和扩展复杂的隐私恢复集体安排。为此，我们首次在严格的高真实感生活实验室实验中比较了态度、内在、奖励和协调数据共享，涉及bbb27,000个真实数据披露。利用因果推理和聚类分析，我们区分了预测隐私和五种关键数据共享行为的标准。引人注目的是，数据共享协调被证明是一个双赢的局面：人们的隐私得到显著恢复，服务提供商的成本明显降低。
Abstract 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 the 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.