具有内生选择的数据共享与差分隐私水平
Data Sharing with Endogenous Choices over Differential Privacy Levels
AI总结：
本文研究了在差分隐私下，玩家具有异质隐私偏好时，大规模数据共享联盟能否通过去中心化方式形成。研究分析了去中心化数据共享机制中玩家的参与和隐私噪声添加决策，揭示了隐私选择带来的权衡：更高的隐私降低个体隐私成本但减少联盟的数据效用和统计准确性。研究发现完全去中心化的数据共享在社会福利和估计精度上效率低下，而简单的部分去中心化机制能缩小这一效率差距。
摘要：
受快速推动数据去中心化共享的激励，我们研究了在差分隐私下，当玩家具有异质隐私偏好时，大规模数据共享联盟能否通过去中心化方式形成。我们首先考虑一个完全去中心化的数据共享机制，在该机制中，每个玩家在共享前决定是否参与以及本地添加多少隐私噪声到敏感数据。隐私选择导致一个根本性的权衡：更高的隐私降低个体隐私成本但减少联盟的数据效用和统计准确性。这些选择产生跨玩家的外部性，使参与和隐私水平成为战略问题。我们的目标是理解哪些联盟是稳定的，隐私选择如何塑造均衡结果，以及当玩家数量很大时，完全去中心化的数据共享与集中化、社会最优基准相比如何。我们分析了多个隐私成本领域，对应于差分隐私的不同攻击/观察模型，显示完全去中心化在社会福利和估计精度上效率低下。令人惊讶的是，我们发现一个简单的部分去中心化机制（玩家仍保留参与权，但中央设计师为所有人选择固定隐私噪声水平）能将这一效率差距缩小到所有隐私成本领域中的常数因子。
英文摘要：
Motivated by the rapid push to decentralize sharing of data, we study whether large-scale data sharing coalitions can form in a decentralized manner under differential privacy when players have heterogeneous privacy preferences. We first consider a fully decentralized data-sharing mechanism in which each player decides whether to participate and how much privacy noise to add locally to their sensitive data before sharing. Privacy choices induce a fundamental trade-off: higher privacy lowers individual privacy costs but reduces data utility and statistical accuracy for the coalition. These choices generate externalities across players, making both participation and privacy levels strategic. Our goal is to understand which coalitions are stable, how privacy choices shape equilibrium outcomes, and how fully decentralized data-sharing compares to a centralized, socially optimal benchmark when the number of players is large. We provide a comprehensive analysis across multiple privacy-cost regimes corresponding to different attack/observation models in differential privacy, showing that full decentralization is highly inefficient in terms of both social welfare and estimator accuracy. Surprisingly, we find that a simple partially decentralized mechanism (where players still retain participation agency, but a central designer chooses a fixed privacy noise level for everyone) closes this efficiency gap down to constant factors across all privacy-cost regimes.
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