Lower Bounds for Differentially Private RAMs
Lower Bounds for Differentially Private RAMs
复制标题
差分私有 RAM 的下限
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Kevin Yeo
中科院分区：
文献类型：
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作者：
G. Persiano;Kevin Yeo
在这项工作中，我们研究了隐私保护的存储原语，适用于在外包数据库的差异隐私框架内的数据分析。差异隐私数据分析的目标是在不损害任何个人隐私的情况下公开群体的全局属性。通常情况下，差异私有对手只会学习全局属性。对于外包数据库的情况，对手还查看访问数据的模式。不经意RAM（ORAM）可用于隐藏访问模式，但ORAM可能过多，因为在某些设置中，它可能足以与差异隐私兼容，并且仅保护个人访问的隐私。
In this work, we study privacy-preserving storage primitives that are suitable for use in data analysis on outsourced databases within the differential privacy framework. The goal in differentially private data analysis is to disclose global properties of a group without compromising any individual’s privacy. Typically, differentially private adversaries only ever learn global properties. For the case of outsourced databases, the adversary also views the patterns of access to data. Oblivious RAM (ORAM) can be used to hide access patterns but ORAM might be excessive as in some settings it could be sufficient to be compatible with differential privacy and only protect the privacy of individual accesses.