
论文笔记:Local Differential Privacy: a tutorial - 知乎
本地差分隐私(ldp)是一种最先进的方法,在保护每个用户隐私的同时允许统计计算。 与差分隐私(DP)不同,由于在本地向用户输入添加了噪声,因此不需要一个可信的数据中心。
local differential privacy summary - 知乎 - 知乎专栏
根据pure LDP的定义, p^* 指的是,当某一个输入被扰动后的结果“支持”时,把这一输入还映射到这个结果“支持”的item上去。 也就是说,这里我们的 p^* 是指,当 H(v) = b 时,我们对 b 扰动,结果依然还是 b 的概率。
4. Local Differential Privacy - 知乎 - 知乎专栏
为应对Untrusted Curator model,可以采用:Local Differential Privacy (LDP),Intermediate Trust Models,Federated Learning等,这里仅介绍LDP。 LDP:终端自行利用Differential Privacy机制,最后将爹加噪声的结果汇总到Curator处。 Random Response满足 (ε,0)-LDP: 假设有n个用户,每个用户可以选择应答的结果都有K个可能,应答Curator时遵循如下Random Response:
局部差分隐私(Local Differential Privacy)的概念如何理解? - 知乎
拿Uber的SQL DP论文 (参考1)来举例,可以很好的解释这个差别,SQL DP同时引入一个新的敏感度方法:弹性敏感度 (Elastic Sensitivity)来解决局部差分隐私可能导致噪音无界的问题。 可以看到,在这种跨域联合查询中,在本地差分的基础上,结合一系列具体的全局差分隐私控制手段,可以在可用性和隐私保护上面,获得一个更好的”双赢“结局。 《2021网络安全西湖学术论坛线上报告》11.22上午场由Tianhao Wang报告,报告内容为《Analyzing Sensitive Data with Local …
Local differential privacy - Wikipedia
Local differential privacy (LDP) is a model of differential privacy with the added requirement that if an adversary has access to the personal responses of an individual in the database, that adversary will still be unable to learn much of the user's personal data. This is contrasted with global differential privacy, a model of differential ...
LDP-Purifier: Defending against Poisoning Attacks in Local
2024年10月27日 · We propose the defense protocol LDP-Purifier to rectify the poisoned histogram and suppress the utility damage by poisoning. As a post-processing method, it can be applied to a wide range of LDP protocols for histogram estimation. We conduct extensive experiments on real-world datasets to demonstrate the effectiveness of our defense method.
LDP DESIGNS
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