A Novel Analysis of Utility in Privacy Pipelines, Using Kronecker Products and Quantitative Information Flow-Reference-Cited by-同舟云学术

A Novel Analysis of Utility in Privacy Pipelines, Using Kronecker Products and Quantitative Information Flow

Published:2023-11-15 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security
language:
Short-container-title:

Author:

Alvim Mário S.¹^ORCID,Fernandes Natasha²^ORCID,McIver Annabelle³^ORCID,Morgan Carroll⁴^ORCID,Nunes Gabriel H.⁵^ORCID

Affiliation:

1. UFMG, Belo Horizonte, Brazil

2. Macquarie University, Sydney, Australia

3. Macquarie University, Sydney, NSW, Australia

4. UNSW & Trustworthy Systems, Sydney, NSW, Australia

5. Macquarie University & UFMG, Sydney, NSW, Australia

Funder

European Research Council

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3576915.3623081

Reference53 articles.

1. A. Ghosh, T. Roughgarden, and M. Sundararajan, "Universally utility-maximizing privacy mechanisms," in Proceedings of the 41st Annual ACM Symposium on Symposium on Theory of Computing - STOC '09, (Bethesda, MD, USA), p. 351, ACM Press, 2009.

2. Optimization of privacy-utility trade-offs under informational self-determination

3. T. Li and N. Li, "On the tradeoff between privacy and utility in data publishing," in Knowledge Discovery and Data Mining, 2009.

4. Q. Geng W. Ding R. Guo and S. Kumar "Tight analysis of privacy and utility tradeoff in approximate differential privacy " in Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (S. Chiappa and R. Calandra eds.) vol. 108 of Proceedings of Machine Learning Research pp. 89--99 PMLR 26-28 Aug 2020.

5. M. S. Alvim, N. Fernandes, A. McIver, and G. H. Nunes, "On privacy and accuracy in data releases (invited paper)," in 31st International Conference on Concurrency Theory, CONCUR 2020, September 1-4, 2020, Vienna, Austria (Virtual Conference) (I. Konnov and L. Kovács, eds.), vol. 171 of LIPIcs, pp. 1:1--1:18, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020.

Cited by 3 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. SoK: A Review of Differentially Private Linear Models For High-Dimensional Data;2024 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML);2024-04-09

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

3. A Novel Analysis of Utility in Privacy Pipelines, Using Kronecker Products and Quantitative Information Flow;Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security;2023-11-15