Collective Privacy Recovery: Data-sharing Coordination via Decentralized Artificial Intelligence
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866929385829826560 |
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| author | Pournaras, Evangelos Ballandies, Mark Christopher Bennati, Stefano Chen, Chien-fei |
| author_facet | Pournaras, Evangelos Ballandies, Mark Christopher Bennati, Stefano Chen, Chien-fei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_05995 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Collective Privacy Recovery: Data-sharing Coordination via Decentralized Artificial Intelligence Pournaras, Evangelos Ballandies, Mark Christopher Bennati, Stefano Chen, Chien-fei Artificial Intelligence Computers and Society Distributed, Parallel, and Cluster Computing Information Retrieval Multiagent Systems 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. |
| title | Collective Privacy Recovery: Data-sharing Coordination via Decentralized Artificial Intelligence |
| topic | Artificial Intelligence Computers and Society Distributed, Parallel, and Cluster Computing Information Retrieval Multiagent Systems |
| url | https://arxiv.org/abs/2301.05995 |