A Course on Differential Privacy (差分隐私) 9.4万播放 Lecture 1A: Some Attempts at Data Privacy - NYC Taxis and Netflix 27:22 Lecture 1B: Some Attempts at Data Privacy - Neural Networks, Medical Studies 30:00 Lecture 2A: Reconstruction and a Census 21:31 Lecture 2B: Reconstruction by Di...
19:49 Lecture 17A: DP Deployments 1, Local DP 51:27 Lecture 17B: DP Deployments 1, Local DP 42:06 Lecture 18: DP Deployments 2 1:24:46 【论文分享】差分隐私~《deep learning with differential privacy》~Moments Accoutant的关键思想
Ashwin Machanavajjhala, Xi He, and Michael Hay. Differential privacy in the wild: A tutorial on current practices & open challenges. PVLDB, 9(13):1611- 1614, 2016.Ashwin Machanavajjhala, Xi He, Michael Hay (2016): Differential Privacy in the Wild: A tutorial on current practices & ...
Differential Privacy: A Survey of Results 来自 Semantic Scholar 喜欢 1 阅读量: 3819 作者: C Dwork 摘要: Over the past five years a new approach to privacy-preserving data analysis has born fruit [13, 18, 7, 19, 5, 37, 35, 8, 32]. This approach differs from much (but not all...
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Differential privacy is a precise mathematical constraint meant to ensureprivacy of individual pieces of information in a database even while queriesare being answered about the aggregate. Intuitively, one must come to termswith what differential privacy does and does not guarantee. For example, thede...
1.1 Introduction to legal and ethical frameworks for research data privacy 一般来说,研究政策要求研究人员保护隐私,将其作为维护受试者尊严和福利的基本原则。因此,研究人员有责任实施隐私保护措施,并将命令的保护范围生态地传达给他们的受试者。此外,根据隐私法和研究机构、资助组织的政策,规范fic的行政、技术和...
差分隐私的概念最早是由Dwork在2006年的一篇论文中提出来的,Dwork是一位计算机科学家,在提出Differential Privacy的时候也有很多计算机domain的考量,但是由于最终的定义是要引入概率的视角,所以在统计的领域内也有学者对这个话题进行研究。而作为一个统计的学生,也应当更多的站在统计的视角去看隐私保护这个topic,所以我读...
In this paper, we first describe the basic concept of DP and then survey its three variants: (a) geo-indistinguishability, (b) private spatial decomposition, and (c) local differential privacy, which are designed or can be used to protect location privacy in LBSs. Furthermore, we explore...
2. Local Differential Privacy 在差分隐私中,通过随机化引入噪声来实现对用户数据的合理否认来完成隐私数据的保护。 首先介绍中心化差分隐私的概念:\epsilon- differential privacy(\epsilon- DP) 定义1:首先给出两个不同的数据集D_1,D_2,一个随机扰动函数\mathcal{K},\mathcal{S}\subseteq Range(\mathcal{K...