---
title: "Privacy for Free in Efficient Agnostic Halfspace Learning"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.05208v1"
permalink: "https://feed.astralor.com/items/itm_43e4964b2cef6382602bacfc"
published: "2026-11-03T16:56:39.000Z"
score: 54
channel: "tech"
tags: ["Privacy","Free","Efficient Agnostic Halfspace","Learning","Gaus","Gaussian"]
---

# Privacy for Free in Efficient Agnostic Halfspace Learning

## At a glance

We provide the first computationally efficient, differentially private algorithms for agnostically learning halfspaces. In particular, we study three different settings of agnostic learning: improper learning under Gaus…

## Key points

- We provide the first computationally efficient, differentially private algorithms for agnostically learning halfspaces.
- In particular, we study three different settings of agnostic learning: improper learning under Gaussian and log-concave marginals; proper learning under Gaussian marginals; and semi-agnostic learning under Gaussian marg…
- In all three aforementioned settings, the added cost in the sample complexity due to privacy matches the respective non-private sample complexity, implying that we get privacy for free.

## Full text

We provide the first computationally efficient, differentially private algorithms for agnostically learning halfspaces. In particular, we study three different settings of agnostic learning: improper learning under Gaussian and log-concave marginals; proper learning under Gaussian marginals; and semi-agnostic learning under Gaussian marginals. In all three aforementioned settings, the added cost in the sample complexity due to privacy matches the respective non-private sample complexity, implying that we get privacy for free.

---

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