Privacy preserving association rule mining in vertically partitioned data

By Siying Gu. 2006

903 citations recorded by the source corpus.

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In Privacy & Security

Citations in the corpus

Learning Privately over Distributed Features: An ADMM Sharing Approach (2019, background): “The motivation of gleaning insights from vertically partitioned data dates back to association rule mining Vaidya and Clifton (2002, 2003).”
Privacy Preserving Data Mining (2015, background): “Review of the Privacy Preserving Data Mining Techniques Based on different dimensions the PPDM techniques are classified into five categories [11]: (1) Randomized Response based PPDM (2) Perturbation based PPDM (3) Anonymization based PPDM (4) Condensation approach based PPDM (5) Cryptography based ”
The Value of Collaboration in Convex Machine Learning with Differential Privacy (2019, methodology): “In the past, secure multi-party computation has been used in various ML models, such as decision trees [5], regression [6], association rules [7], and clustering [8],”
Privacy-Preserving Multivariate Statistical Analysis: Linear Regression and Classification (2004, methodology): “The solution proposed in [25] is more similar to the solution we proposed here than other solutions, because they both use a disguise matrix (in different ways) to mix data together for the privacy purpose.”
Secure Regression on Distributed Databases (2005, background): “Other problems that have been treated include association rules (Vaidya and Clifton, 2002; Evﬁmievski et al., 2002; Kantarcioglu and Clifton, 2002), classiﬁcation (Du et al., 2004), clustering (Vaidya and Clifton, 2003; Lin et al., 2004), and linear regression for vertically partitioned data (Du et…”
A survey on trust management for Internet of Things (2014, background): “It is widely used in privacy preserving collaborative computation: privacy preserving computational geometry [44], privacy preserving statistic analysis [45], privacy preserving matrix computation [46], privacy preserving scientific computation [47], especially privacy preserving data mining [48-52]”
Privacy Preserving Decision Tree Learning Using Unrealized Data Sets (2012, background): “SMC research focuses on protocol development [11] for protecting privacy among the involved parties [12] or computation efficiency [13]; however, centralized processing of samples and storage privacy is out of the scope of SMC.”
Privacy-preserving collaborative data mining (2007, background): “[18] showed that these two notions actually coincide.”
Query Expansion Techniques for Information Retrieval: a Survey (2017, neutral): “This well known approach [22] is identical to the association rule used in data mining problem [6, 258].”
A Framework for Evaluating Privacy Preserving Data Mining Algorithms* (2005, methodology): “A cryptography-based technique is proposed by Kantarcioglu and Clifton (2002).”
Privacy Preserving Data Mining Research: Current Status and Key Issues (2007, background): “Horizontal distributions refer to the cases where different records of the same data attributes are resided in different places [7] - [11] [26] ; while in a vertical data distribution, different attributes of the same record of data are resided in different places [2] [23] [24] [25] [30] [33] .”
Privacy-preserving outsourced classification in cloud computing (2017, background): “A lot of works have been supposed for privacy-preserving training algorithm, such as back-propagation networks [16, 24], naïve bayes [25,26], K-means clustering [27–29], association rules [30,31], linear regression [32,33], decision trees [31,34] and so forth.”
Database security - concepts, approaches, and challenges (2005, methodology): “The development of contentbased access control models, which are, in general, based on the specification of conditions against data contents, was made easy in relational databases by the availability of declarative query languages, such as SQL.”
Threats to Federated Learning: A Survey (2020, positive): “VFL is applicable to the cases in which participants have large overlaps in the sample space but differ in the feature space, i.e. , different parties hold different attributes of the same records [Vaidya and Clifton, 2002].”
Efficient k -Anonymization Using Clustering Techniques (2007, background): “4791∗ Person [35-39] Cancer 4790∗ Person [30-34] HIV+ 4791∗ Person [35-39] Flu 4791∗ Person [35-39] Obesity 4790∗ Person [30-34] Cancer 4790∗ Person [30-34] Flu”
A Survey of Privacy-Preserving Methods Across Vertically Partitioned Data (2008, methodology): “[25] first showed how secure association rule mining can be done for vertically partitioned data by extending the apriori algorithm.”
Privacy Preserving Naïve Bayes Classifier for Vertically Partitioned Data (2004, neutral): “Keywords: privacy, security, distributed classification”
State-of-the-art in privacy preserving data mining (2004, methodology): “tions masked with random values, is described in [23].”
On Private Scalar Product Computation for Privacy-Preserving Data Mining (2004, methodology): “A secure scalar product protocol has various applications in privacypreserving data mining, starting with privacy-preserving frequent pattern mining on vertically distributed database [ VC02 ] and ending with privacy-preserving cooperative statistical analysis [DA01a].”