Bottom-up generalization: a data mining solution to privacy protection

By Ke Wang, Philip S. Yu, Sourav Chakraborty. Industrial Conference on Data Mining, 2004

380 citations recorded by the source corpus.

The well-known privacy-preserved data mining modifies existing data mining techniques to randomized data. In this paper, we investigate data mining as a technique for masking data, therefore, termed data mining based privacy protection. This approach incorporates partially the requirement of a targeted data mining task into the process of masking data so that essential structure is preserved in the masked data. The idea is simple but novel: we explore the data generalization concept from data mining as a way to hide detailed information, rather than discover trends and patterns. Once the data is masked, standard data mining techniques can be applied without modification. Our work demonstrated another positive use of data mining technology: not only can it discover useful patterns, but also mask private information. We consider the following privacy problem: a data holder wants to release a version of data for building classification models, but wants to protect against linking the released data to an external source for inferring sensitive information. We adapt an iterative bottom-up generalization from data mining to generalize the data. The generalized data remains useful to classification but becomes difficult to link to other sources. The generalization space is specified by a hierarchical structure of generalizations. A key is identifying the best generalization to climb up the hierarchy at each iteration. Enumerating all candidate generalizations is impractical. We present a scalable solution that examines at most one generalization in each iteration for each attribute involved in the linking.

Subfields: Artificial Intelligence, Computer Science Applications, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/010dd165b75d5de6a88fea68be94c34dcd7756ee

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Citations in the corpus

Anonymizing Classification Data for Privacy Preservation (2007, methodology): “Greedy methods were proposed in [10], [11], [12], [13], [14], and [15].”
On the tradeoff between privacy and utility in data publishing (2009, background): “Classification accuracy on the anonymized data has been evaluated in [18, 28, 10, 27, 5].”
Deriving private information from randomized data (2005, methodology): “used an iterative bottom-up generalization to generate data, which remains useful to classification but difficult to disclose private sources [25].”
A General Survey of Privacy-Preserving Data Mining Models and Algorithms (2008, background): “Therefore, even though the data is k -anonymized, the value of the sensitive attribute for that group of k records can be predicted exactly.”
A comprehensive review on privacy preserving data mining (2015, methodology): “(Wang et al. 2004) studied the data mining as a approach used for data masking called data mining based on privacy protection.”
Personalized privacy preservation (2006, methodology): “Since the introduction of k-anonymity in [13, 15], numerous algorithms [5, 7, 8, 9, 10, 13, 15, 17] have been proposed to obtain kanonymous tables.”
Incognito: efficient full-domain K-anonymity (2005, methodology): “Recently, top-down [7] and bottom-up [19] greedy heuristic algorithms were proposed for producing anonymous data that remains useful for building decision-tree classifiers.”
k -Anonymous Data Mining: A Survey (2008, neutral): “been married M [40-100) 17 been married F [1-40) 2 been married F [40-100) 9 never married M [40-100) 26”
Privacy-preserving data publishing: A survey of recent developments (2010, methodology): “Few works [Fung et al. 2005; 2007; Iyengar 2002; LeFevre et al. 2006b; Wang et al. 2004] have actually conducted such experiments, although many such as [Bayardo and Agrawal 2005] adopted CM in an attempt to address the classification problem.”
Application of Local Differential Privacy to Collection of Indoor Positioning Data (2018, background): “Wang et al. proposed a bottom-up generalization algorithm to ﬁnd a minimal k -anonymization for classiﬁcation [31].”
Deep Private-Feature Extraction (2018, background): “Many researches are presented to make a database k-anonymous [14], [15], [16], [17] and most of them are based on the generalization (e.”
Anatomy: simple and effective privacy preservation (2006, background): “RELATED WORK Generalization has been very well studied in the literature [1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18].”
Privacy Preserving Data Mining Research: Current Status and Key Issues (2007, background): “In a centralized database (C-DB) environment, data are all stored in a single database; while, in a distributed database (D-DB) environment, data are stored in different databases [3]-[6] [13]-[22] [27] [29] [31] .”
Top-down specialization for information and privacy preservation (2005, methodology): “Now, the two classes of the top 24 records become indistinguishable because they are all generalized into <10th, ANY Sex, [1-99)>.”
Minimality Attack in Privacy Preserving Data Publishing (2007, methodology): “This data set (5.5MB) was also adopted by [10, 12, 20, 6].”
Mondrian Multidimensional K-Anonymity (2006, methodology): “There are a number of notions of k-anonymization quality [2, 6, 8, 10, 12, 13, 14, 15, 16], but intuitively, anonymization should generalize or perturb the data as little as is necessary to satisfy the k-anonymity constraint.”
Anonymity-preserving data collection (2005, background): “Another class of closely related work is k-anonymization [38, 37, 36, 35, 10, 28, 5, 41, 4, 47], which processes a database table to de-associate privacy-sensitive attributes from the corresponding identifiers.”