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

arXiv: https://arxiv.org/abs/2301.05995 · HTML: https://mlsanity.com/paper/2301.05995/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 75 references that are in the map.
- First posted on arXiv 15 Jan 2023.

## Builds on (most cited first, 6 of 6)

- [Socio-technical Smart Grid Optimization via Decentralized Charge Control of Electric Vehicles](https://mlsanity.com/paper/1701.06811/) (2017)
- [How Value-Sensitive Design Can Empower Sustainable Consumption](https://mlsanity.com/paper/2004.09180/) (2020)
- [To incentivize or not: Impact of blockchain-based cryptoeconomic tokens on human information sharing behavior](https://mlsanity.com/paper/2206.03221/) (2022)
- [Holarchic Structures for Decentralized Deep Learning - A Performance Analysis](https://mlsanity.com/paper/1805.02686/) (2018)
- [A Study of Age and Gender seen through Mobile Phone Usage Patterns in Mexico](https://mlsanity.com/paper/1511.06656/) (2015)
- [Modelling imperfect knowledge via location semantics for realistic privacy risks estimation in trajectory data](https://mlsanity.com/paper/2011.09218/) (2020)

## Built on by (most cited first, 2 of 2)

- [Science in the Era of ChatGPT, Large Language Models and Generative AI: Challenges for Research Ethics and How to Respond](https://mlsanity.com/paper/2305.15299/) (2023)
- [Artificial Intelligence and Innovation Ecosystem: Evolutionary Developments, Challenges, and Future Directions](https://mlsanity.com/paper/2607.24589/) (2026)

## Abstract (arXiv)

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.