Published In
IEEE Transactions on Knowledge & Data Engineering
Document Type
Post-Print
Publication Date
12-2023
Subjects
Inference Control, Inference -- Mathematical models
Abstract
Simply restricting the computation to non-sensitive part of the data may lead to inferences on sensitive data through data dependencies. Inference control from data dependencies has been studied in the prior work. However, existing solutions either detect and deny queries which may lead to leakage – resulting in poor utility, or only protects against exact reconstruction of the sensitive data – resulting in poor security. In this paper, we present a novel security model called full deniability. Under this stronger security model, any information inferred about sensitive data from non-sensitive data is considered as a leakage. We describe algorithms for efficiently implementing full deniability on a given database instance with a set of data dependencies and sensitive cells. Using experiments on two different datasets, we demonstrate that our approach protects against realistic adversaries while hiding only minimal number of additional non-sensitive cells and scales well with database size and sensitive data.
Rights
© Copyright the author(s) 2023
Locate the Document
DOI
10.1109/TKDE.2023.3336630
Persistent Identifier
https://archives.pdx.edu/ds/psu/40992
Citation Details
Pappachan, P., Zhang, S., He, X., & Mehrotra, S. (2023). Preventing Inferences through Data Dependencies on Sensitive Data. IEEE Transactions on Knowledge & Data Engineering, (01), 1-18.
Description
This article has been accepted for publication in IEEE Transactions on Knowledge and Data Engineering. This is the author's version which has not been fully edited and content may change prior to final publication. Citation information: DOI 10.1109/TKDE.2023.3336630