Alexander Yu

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20232025

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cs.CR2025

Approximate Differential Privacy of the $ℓ_{2}$ Mechanism

Matthew Joseph, Alex Kulesza, Alexander Yu

We study the $ℓ_{2}$ mechanism for computing a $d$ -dimensional statistic with bounded $ℓ_{2}$ sensitivity under approximate differential privacy. Across a range of privacy parame…

cs.CR2024

Privately Counting Partially Ordered Data

Matthew Joseph, Mónica Ribero, Alexander Yu

We consider differentially private counting when each data point consists of $d$ bits satisfying a partial order. Our main technical contribution is a problem-specific $K$ -norm mec…

cs.CR2023

Some Constructions of Private, Efficient, and Optimal $K$ -Norm and Elliptic Gaussian Noise

Matthew Joseph, Alexander Yu

Differentially private computation often begins with a bound on some $d$ -dimensional statistic's $ℓ_{p}$ sensitivity. For pure differential privacy, the $K$ -norm mechanism can imp…

Approximate Differential Privacy of the ℓ2​ Mechanism

Privately Counting Partially Ordered Data

Some Constructions of Private, Efficient, and Optimal K-Norm and Elliptic Gaussian Noise

Approximate Differential Privacy of the $ℓ_{2}$ Mechanism

Some Constructions of Private, Efficient, and Optimal $K$ -Norm and Elliptic Gaussian Noise