Skip to Main content Skip to Navigation
New interface
Conference papers

A Correlation-Preserving Fingerprinting Technique for Categorical Data in Relational Databases

Abstract : Fingerprinting is a method of embedding a traceable mark into digital data, to verify the owner and identify the recipient a certain copy of a data set has been released to. This is crucial when releasing data to third parties, especially if it involves a fee, or if the data is of sensitive nature, due to which further sharing and leaks should be discouraged and deterred from. Fingerprinting and watermarking are well explored in the domain of multimedia content, such as images, video, or audio.The domain of relational databases is explored specifically for numerical data types, for which most state-of-art techniques are designed. However, many datasets also, or even exclusively, contain categorical data.We, therefore, propose a novel approach for fingerprinting categorical type of data, focusing on preserving the semantic relations between attributes, and thus limiting the perceptibility of marks, and the effects of the fingerprinting on the data quality and utility. We evaluate the utility, especially for machine learning tasks, as well as the robustness of the fingerprinting scheme, by experiments on benchmark data sets.
Document type :
Conference papers
Complete list of metadata
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Monday, November 22, 2021 - 3:34:06 PM
Last modification on : Monday, November 22, 2021 - 4:37:36 PM
Long-term archiving on: : Wednesday, February 23, 2022 - 8:00:14 PM


 Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed until : 2023-01-01

Please log in to resquest access to the document


Distributed under a Creative Commons Attribution 4.0 International License




Tanja Sarcevic, Rudolf Mayer. A Correlation-Preserving Fingerprinting Technique for Categorical Data in Relational Databases. 35th IFIP International Conference on ICT Systems Security and Privacy Protection (SEC), Sep 2020, Maribor, Slovenia. pp.401-415, ⟨10.1007/978-3-030-58201-2_27⟩. ⟨hal-03440847⟩



Record views