The vast majority of artificial intelligence solutions are founded on game theory, and differential privacy is emerging as perhaps the most rigorous and widely adopted privacy paradigm in the field. However, alongside all the advancements made in both these fields, there is not a single application that is not still vulnerable to privacy violations, security breaches, or manipulation by adversaries. Our understanding of the interactions between differential privacy and game theoretic solutions is limited. Hence, we undertook a comprehensive review of literature in the field, finding that differential privacy has several advantageous properties that can make more of a contribution to game theory than just privacy protection. It can also be used to build heuristic models for game-theoretic solutions, to avert strategic manipulations, and to quantify the cost of privacy protection. With a focus on mechanism design, the aim of this article is to provide a new perspective on the currently held impossibilities in game theory, potential avenues to circumvent those impossibilities, and opportunities to improve the performance of game-theoretic solutions with differentially private techniques.

中文翻译：

---

不仅仅是隐私

绝大多数人工智能解决方案都建立在博弈论之上，而差分隐私可能正在成为该领域最严格和被广泛采用的隐私范式。然而，除了在这两个领域取得的所有进步外，没有一个应用程序仍然容易受到隐私侵犯、安全漏洞或对手操纵的影响。我们对差分隐私和博弈论解决方案之间相互作用的理解是有限的。因此，我们对该领域的文献进行了全面回顾，发现差异隐私具有几个有利的特性，可以对博弈论做出更多的贡献，而不仅仅是隐私保护。它还可用于为博弈论解决方案构建启发式模型，以避免战略操作，并量化隐私保护的成本。本文以机制设计为重点，旨在为博弈论中当前存在的不可能性、规避这些不可能性的潜在途径以及使用差分私有技术提高博弈论解决方案性能的机会提供一个新视角。