Unifying Privacy Measures via Maximal (α, β)-Leakage (MαbeL)-Reference-Cited by-同舟云学术

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Unifying Privacy Measures via Maximal (α, β)-Leakage (MαbeL)

Published:2024-06 Issue:6 Volume:70 Page:4368-4395
ISSN:0018-9448
Container-title:IEEE Transactions on Information Theory
language:
Short-container-title:IEEE Trans. Inform. Theory

Author:

Gilani Atefeh¹^ORCID,Kurri Gowtham R.¹^ORCID,Kosut Oliver¹^ORCID,Sankar Lalitha¹^ORCID

Affiliation:

1. School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA

Funder

NSF

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

https://ieeexplore.ieee.org/ielam/18/10536118/10491106-aam.pdf

Reference58 articles.

1. An Alphabet of Leakage Measures

2. Differential Privacy

3. On the Foundations of Quantitative Information Flow

4. Quantitative Notions of Leakage for One-try Attacks

5. What Can We Learn Privately?

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Summary Statistic Privacy in Data Sharing;IEEE Journal on Selected Areas in Information Theory;2024

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