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

TL;DR

This paper demonstrates that decentralized AI can effectively coordinate data sharing among individuals, significantly enhancing privacy while maintaining service quality, based on a large-scale real-world experiment.

Contribution

It introduces a novel decentralized AI approach for automating complex collective data-sharing arrangements to recover privacy, validated through a large-scale living-lab experiment.

Findings

Privacy improves significantly through coordinated data sharing.

Data-sharing coordination reduces costs for service providers.

Different criteria predict privacy and data-sharing behaviors.

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.