Probabilistic Fingerprinting Scheme for Correlated Data

Authors

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

, "Probabilistic Fingerprinting Scheme for Correlated Data" (2023). Faculty, Staff and Student Publications. 896.
https://digitalcommons.library.tmc.edu/uthshis_docs/896