Collective Privacy Recovery: Data-sharing Coordination via Decentralized Artificial Intelligence

• URL: http://arxiv.org/abs/2301.05995v2
• Date: Mon, 10 Jul 2023 21:51:34 GMT
• Title: Collective Privacy Recovery: Data-sharing Coordination via Decentralized Artificial Intelligence
• Authors: Evangelos Pournaras, Mark Christopher Ballandies, Stefano Bennati, Chien-fei Chen
• Abstract summary: We show how to automate and scale-up complex collective arrangements for privacy recovery. We compare for first time attitudinal, intrinsic, rewarded and coordinated data sharing. Strikingly, data-sharing coordination proves to be a win-win for all.
• Score: 2.309914459672557
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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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