Zobrazeno 1 - 10
of 109
pro vyhledávání: '"Awan, Jordan A."'
Autor:
Cho, Young Hyun, Awan, Jordan
Motivated by the 2020 US Census products, this paper extends differential privacy (DP) to address the joint release of DP outputs and nonprivate statistics, referred to as invariant. Our framework, Semi-DP, redefines adjacency by focusing on datasets
Externí odkaz:
http://arxiv.org/abs/2410.17468
Autor:
Ohnishi, Yuki, Awan, Jordan
Differential privacy is the leading mathematical framework for privacy protection, providing a probabilistic guarantee that safeguards individuals' private information when publishing statistics from a dataset. This guarantee is achieved by applying
Externí odkaz:
http://arxiv.org/abs/2410.14789
In differential privacy (DP) mechanisms, it can be beneficial to release "redundant" outputs, in the sense that a quantity can be estimated by combining different combinations of privatized values. Indeed, this structure is present in the DP 2020 Dec
Externí odkaz:
http://arxiv.org/abs/2409.04387
We develop both theory and algorithms to analyze privatized data in the unbounded differential privacy(DP), where even the sample size is considered a sensitive quantity that requires privacy protection. We show that the distance between the sampling
Externí odkaz:
http://arxiv.org/abs/2406.06231
Autor:
Awan, Jordan, Ramasethu, Aishwarya
In this paper, we establish anti-concentration inequalities for additive noise mechanisms which achieve $f$-differential privacy ($f$-DP), a notion of privacy phrased in terms of a tradeoff function $f$ which limits the ability of an adversary to det
Externí odkaz:
http://arxiv.org/abs/2308.08343
This paper is the first to attempt differentially private (DP) topological data analysis (TDA), producing near-optimal private persistence diagrams. We analyze the sensitivity of persistence diagrams in terms of the bottleneck distance, and we show t
Externí odkaz:
http://arxiv.org/abs/2305.03609
Autor:
Awan, Jordan, Wang, Zhanyu
Privacy protection methods, such as differentially private mechanisms, introduce noise into resulting statistics which often produces complex and intractable sampling distributions. In this paper, we propose a simulation-based "repro sample" approach
Externí odkaz:
http://arxiv.org/abs/2303.05328
Autor:
Ohnishi, Yuki, Awan, Jordan
Local differential privacy is a differential privacy paradigm in which individuals first apply a privacy mechanism to their data (often by adding noise) before transmitting the result to a curator. The noise for privacy results in additional bias and
Externí odkaz:
http://arxiv.org/abs/2301.01616
Differentially private (DP) mechanisms protect individual-level information by introducing randomness into the statistical analysis procedure. Despite the availability of numerous DP tools, there remains a lack of general techniques for conducting st
Externí odkaz:
http://arxiv.org/abs/2210.06140
Autor:
Awan, Jordan, Wang, Yue
Hypothesis testing is a central problem in statistical analysis, and there is currently a lack of differentially private tests which are both statistically valid and powerful. In this paper, we develop several new differentially private (DP) nonparam
Externí odkaz:
http://arxiv.org/abs/2208.06236