Faculty, Staff and Student Publications
Language
English
Publication Date
7-1-2023
Journal
Data and Applications Security and Privacy XXXVII
DOI
10.1007/978-3-031-37586-6_5
PMID
41426237
PMCID
PMC12716443
PubMedCentral® Posted Date
12-20-2025
PubMedCentral® Full Text Version
Author MSS
Abstract
In order to receive personalized services, individuals share their personal data with a wide range of service providers, hoping that their data will remain confidential. Thus, in case of an unauthorized distribution of their personal data by these service providers, data owners want to identify the source of such data leakage. We show that applying existing fingerprinting schemes to personal data sharing is vulnerable to the attacks utilizing the correlations in the data. To provide liability for unauthorized sharing of personal data, we propose a probabilistic fingerprinting scheme that efficiently generates the fingerprint by considering a fingerprinting probability (to keep the data utility high) and publicly known inherent correlations between data points. To improve the robustness of the proposed scheme against colluding malicious service providers, we also utilize the Boneh-Shaw fingerprinting codes as a part of the proposed scheme. We implement and evaluate the performance of the proposed scheme on real genomic data. Our experimental results show the efficiency and robustness of the proposed scheme.
Keywords
Fingerprinting, Liability, Data sharing
Published Open-Access
yes
Recommended Citation
Emre Yilmaz and Erman Ayday, "Probabilistic Fingerprinting Scheme for Correlated Data" (2023). Faculty, Staff and Student Publications. 896.
https://digitalcommons.library.tmc.edu/uthshis_docs/896