Smooth Anonymity for Sparse Graphs
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3589335.3651561.pdf
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1.56 MB
Format
Adobe PDF
Checksum (MD5)
eca1873146344ffc7e1038cf5b845c39
Author(s)
Epasto, Alessandro
Esfandiari, Hossein
Mirrokni, Vahab
Munoz Medina, Andres
Date Issued
May 13, 2024
Publisher
ACM
Citation
Epasto, Alessandro, Esfandiari, Hossein, Mirrokni, Vahab and Munoz Medina, Andres. 2024. "Smooth Anonymity for Sparse Graphs."
Version
Final published version
Abstract
In this work, we aim to manipulate and share an entire sparse dataset with a third party privately. As our first main result, we prove that any differentially private mechanism that maintains a reasonable similarity with the initial dataset is doomed to have a very weak privacy guarantee. Next, we consider a variation of k-anonymity, which we call smooth-k-anonymity, and design a simple large-scale algorithm that efficiently provides smooth-k-anonymity. We further perform an empirical evaluation and show that our algorithm improves the performance in downstream machine learning tasks on anonymized data.
Description
WWW '24: Companion Proceedings of the ACM on Web Conference May 13–17, 2024, Singapore, Singapore
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3589335.3651561
