图 书 馆
交互式指纹码和防止错误发现的难度Interactive Fingerprinting Codes and the Hardness of Preventing False Discovery |
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| 课程网址: | https://videolectures.net/videos/colt2015_steinke_false_discovery |
| 主讲教师: | Thomas Steinke |
| 开课单位: | 信息不详。欢迎您在右侧留言补充。 |
| 开课时间: | 2025-02-04 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We show an essentially tight bound on the number of adaptively chosen statistical queries that a computationally efficient algorithm can answer accurately given n samples from an unknown distribution. A statistical query asks for the expectation of a predicate over the underlying distribution, and an answer to a statistical query is accurate if it is "close" to the correct expectation over the distribution. This question was recently studied by Dwork et al., who showed how to answer ~n^2 queries efficiently, and also by Hardt and Ullman, who showed that answering ~n^3 queries is hard. We close the gap between the two bounds and show that, under a standard hardness assumption, there is no computationally efficient algorithm that, given n samples from an unknown distribution, can give valid answers to O(n^2) adaptively chosen statistical queries. An implication of our results is that computationally efficient algorithms for answering arbitrary, adaptively chosen statistical queries may as well be differentially private. We obtain our results using a new connection between the problem of answering adaptively chosen statistical queries and a combinatorial object called an interactive fingerprinting code (Fiat and Tassa '01). In order to optimize our hardness result, we give a new Fourier-analytic approach to analyzing fingerprinting codes that is simpler, more flexible, and yields better parameters than previous constructions. |
| 关 键 词: | 紧密界限; 正确期望; 指纹代码 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2025-03-29：zsp |
| 最后编审: | 2025-03-29：zsp |
| 阅读次数: | 14 |