Collective Privacy Recovery: Data-sharing Coordination via Decentralized Artificial Intelligence
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.
Code (1)
Tasks
Causal InferenceMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Privacy-Engineered Value Decomposition Networks for Cooperative Multi-Agent Reinforcement Learning
In cooperative multi-agent reinforcement learning (Co-MARL), a team of agents must jointly optimize the team's long-term rewards to learn a designated task. Optimizing rewards as a team often requires inter-agent communi…
Multi-agent Reinforcement LearningPrivacy Preservingreinforcement-learningSMAC+2Structured Fluctuations and the Information Dynamics of Self-Maintenance in Growing Neural Cellular Automata
Growing Neural Cellular Automata (GNCA) are capable of robust self-maintenance and self-repair, yet the internal dynamical mechanisms that support these capabilities remain poorly understood. Here, we investigate the rol…
Dimensionality ReductionFault-Tolerant Multi-Robot Coordination with Limited Sensing within Confined Environments
As robots are increasingly deployed to collaborate on tasks within shared workspaces and resources, the failure of an individual robot can critically affect the group's performance. This issue is particularly challenging…
Hierarchical Decentralized Multi-Agent Coordination with Privacy-Preserving Knowledge Sharing: Extending AgentNet for Scalable Autonomous Systems
Decentralized multi-agent systems have shown promise in enabling autonomous collaboration among LLM-based agents. While AgentNet demonstrated the feasibility of fully decentralized coordination through dynamic DAG topolo…
Knowledge DistillationMore Capable, Less Cooperative? When LLMs Fail At Zero-Cost Collaboration
Large language model (LLM) agents increasingly coordinate in multi-agent systems, yet we lack an understanding of where and why cooperation fails. Many real-world coordination problems are not social dilemmas: helping ot…