我们的选择说明了我们的什么偏好？
What Do Our Choices Say About Our Preferences?
AI总结：
本文从差分隐私角度研究在线停止算法，提出一种通过参数控制准确性与隐私权衡的机制，并对其在经典秘书问题中的表现进行了详细分析。
摘要：
做出在线决策是日常生活的一部分。比如买房、停车或参与拍卖。我们经常公开做出这些决策，这可能侵犯我们的隐私——观察我们选择的方可能会了解到很多关于我们偏好的信息。在本文中，我们从隐私保护的角度，使用数学上严格的差分隐私概念来研究在线停止算法。在差分隐私算法中，通常存在平衡隐私和效用的问题。在这种情况下，大多数情况下，同时拥有最优性和高级别隐私是不可能的。我们提出了一种自然机制来实现在线算法的准确性与其隐私之间的可控权衡，该权衡由一个参数量化。根据该参数的不同，我们的机制可以在较弱的差分隐私下达到最优，或者在次优但更注重隐私保护的情况下运行。我们对应用于经典秘书问题最优算法的机制进行了详细的准确性和隐私分析。由此，来自两个不同领域——最优停止和差分隐私——的经典概念首次相遇。
英文摘要：
Taking online decisions is a part of everyday life. Think of buying a house, parking a car or taking part in an auction. We often take those decisions publicly, which may breach our privacy - a party observing our choices may learn a lot about our preferences. In this paper we investigate the online stopping algorithms from the privacy preserving perspective, using a mathematically rigorous differential privacy notion. In differentially private algorithms there is usually an issue of balancing the privacy and utility. In this regime, in most cases, having both optimality and high level of privacy at the same time is impossible. We propose a natural mechanism to achieve a controllable trade-off, quantified by a parameter, between the accuracy of the online algorithm and its privacy. Depending on the parameter, our mechanism can be optimal with weaker differential privacy or suboptimal, yet more privacy-preserving. We conduct a detailed accuracy and privacy analysis of our mechanism applied to the optimal algorithm for the classical secretary problem. Thereby the classical notions from two distinct areas - optimal stopping and differential privacy - meet for the first time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
