Work

Enhanced Privacy Bound for Shuffle Model with Personalized Privacy

Year: 2024

Type: conference-paper

Abstract: -DP, where the convexity of the distributions is leveraged to achieve a tighter privacy bound. Theoretical and numerical results demonstrate that our bound remarkably outperforms the existing results in the literature. The code is publicly available at https://github.com/Emory-AIMS/HPS.git.

Source: ACM International Conference on Information and Knowledge Management (CIKM)

DOI: 10.1145/3627673.3679911

Authors: Yixuan Liu, Yuhan Liu, Li Xiong, Yujie Gu, Hong Chen

Institutions: Renmin University of China, Emory University, Kyushu University

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, National Natural Science Foundation of China, National Institutes of Health, Japan Society for the Promotion of Science, National Institute of Environmental Health Sciences, U.S. National Library of Medicine

Awards: G1910011397, G2877228074, G3763756418, G3972953005, SCC-IRG JST: Hyperlocal Risk Monitoring and Pandemic Preparedness through Privacy-Enhanced Mobility and Social Interactions Analysis, Collaborative Research: NSF-CSIRO: HCC: Small: Understanding Bias in AI Models for the Prediction of Infectious Disease Spread, G7346449361, Collaborative Research: SaTC: CORE: Medium: PREMED: Privacy-Preserving and Robust Computational Phenotyping using Multisite EHR Data, G8038560034, G8385808830, A Study on Secure Codes and Traitor Tracing Algorithms for Copyright Protection, Sensor Hardware and Intelligent Tools for Assessing the Health Effects of Heat Exposure, G9000039797, G9000132468, G997158302

OpenAlex ID: W4403582529

FWCI: 0.589

Cites: 21

Cited by: 2

Related to: 10