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.
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Chen, Chien-fei ; Dietz, Thomas ; Fefferman, Nina H. ; Greig, Jamie ; Cetin, Kristen ; Robinson, Caitlin ; Arpan, Laura ; Schweiker, Marcel ; Dong, Bing ; Wu, Wenbo ; et al ( , Energy Research & Social Science)
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Mammoli, Andrea ; Robinson, Matthew ; Ayon, Victor ; Martínez-Ramón, Manel ; Chen, Chien-fei ; Abreu, Joana M. ( , Energy and Buildings)
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Talele, Suraj ; Traylor, Caleb ; Arpan, Laura ; Curley, Cali ; Chen, Chien-Fei ; Day, Julia ; Feiock, Richard ; Hadzikadic, Mirsad ; Tolone, William J. ; Ingman, Stan ; et al ( , Frontiers in Energy)