Using unknowns to prevent discovery of association rules

By Yücel Saygin, Vassilios S. Verykios, Chris Clifton. SGMD, 2001

394 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/881485ee4da94a746557e4b2da784fc07ef5a0d3

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Citations in the corpus

Mining Association Rules under Privacy Constraints (2008, background): “An alternative approach for output rule privacy proposed in [37, 36] is to use the concept of “data blocking”, wherein some values in the database are replaced with NULLs signifying unknowns.”
Maintaining Data Privacy in Association Rule Mining (2002, background): “Concurrently with our work, the issue of maintaining privacy in association rule mining has attracted considerable attention over the last year [ 14 , 5, 6, 15, 19, 11, 8]. The problem addressed in [14, 5, 6, 15] is how to prevent sensitive rules from being inferred by the data miner { the proposed ”
Random projection-based multiplicative data perturbation for privacy preserving distributed data mining (2006, methodology): “The blockingbased association rule hiding approach [27] replaces certain attributes of the data with a question mark.”
Privacy preserving frequent itemset mining (2002, methodology): “Although the papers mentioned above deal with privacy preserving in association rules, in this paper, our work is directly related to (Atallah et al. 1999, Dasseni et al. 2001, Saygin et al. 2001).”
A framework for high-accuracy privacy-preserving mining (2004, background): “e. It involves speciﬁcation of how the data is to be used in a privacy policy and enforcing limited disclosure rules for regulatory concerns prompted by legislation. Finally, the problem addressed in [19, 10, 11, 20] is how to prevent sensitive rules from being inferred by the data miner – this work”
State-of-the-art in privacy preserving data mining (2004, background): “An extension of this work with a detailed discussion on how effective is this approach on reconstructing the confused rules, can be found in [21].”
Discrimination prevention in data mining for intrusion and crime detection (2011, background): “It is worth mentioning that decreasing the confidence of special rules (sensitive rules) by data transformation was previously used for knowledge hiding [13], [14], [15] in privacy-preserving data mining (PPDM).”
Privacy Preserving Data Mining Research: Current Status and Key Issues (2007, neutral): “Data hiding [3] [4] [5] [6] [17] [18] [20] [29] [31] [2] [7] [8] [9] 10] [11] [23] [24] [25] [27] [30] [33] Rule hiding [14] [15] [16] [19] [27] [21] [22] None”
Preserving privacy by de-identifying face images (2005, background): “The goal in rule hiding [4], [18], [19] is to prevent sensitive rules from being revealed in shared data.”