Collective privacy recovery: Data-sharing coordination via decentralized artificial intelligence.

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 inf

SUBMITTER: Pournaras E 

PROVIDER: S-EPMC10847902 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature