Privacy preserving associative classification on vertically partitioned databases

Raghuram, B.; Gyani, Jayadev

doi:10.1109/icaccct.2012.6320768

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…These approaches generally ensure that every attribute is protected homogeneous together with a global sensitivity without considering partition of the dataset. In other works, 24–31 privacy of multi‐attributes data was preserved via vertical partitioning. Such approaches consider how one can reduce the effect of partitioned data from different places on privacy information disclosure between them.…”

Section: Related Literaturementioning

confidence: 99%

Heterogeneous differential privacy for vertically partitioned databases

Xia

Zhu

Ding

et al. 2019

Concurrency and Computation

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Summary Existing privacy‐preserving approaches are generally designed to provide privacy guarantee for individual data in a database, which reduces the utility of the database for data analysis. In this paper, we propose a novel differential privacy mechanism to preserve the heterogeneous privacy of a vertically partitioned database based on attributes. We first present the concept of privacy label, which characterizes the privacy information of the database and is instantiated by the classification. Then, we use an information‐based method to systematically explore the dependencies between all attributes and the privacy label. We finally assign privacy weights to every attribute and design a heterogeneous mechanism according to the basic Laplace mechanism. Evaluations using real datasets demonstrate that the proposed mechanism achieves a balanced privacy and utility.

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Section: Related Literaturementioning

confidence: 99%

Heterogeneous differential privacy for vertically partitioned databases

Xia

Zhu

Ding

et al. 2019

Concurrency and Computation

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“…An agent based approach for mining fuzzy association rule based classifier presented in [11] but model is based up on centralized approach the major problem of this approach is huge amount of data as to transfer from all sources to centralized source. While all these methods are based on CD approaches, the accurate and communication efficient FDM approach based fuzzy associative classification for distributed databases for horizontal portioning proposed in [12] and for vertical partitioning proposed in [13]. The drawback of all these methods even though they claim better accuracy and communication efficiency the drawback of these models are that they won't handle the incremental data and update classification results.…”

Section: Related Workmentioning

confidence: 99%

Incremental associative classification on distributed databases

Bhukya

Gyani

2014

International Conference for Convergence for Technology-2014

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Distributed Data Mining (DDM) which is a process of extracting knowledge from distributed data without integrating them in a common database. Due to its vast application in real world application distributed data mining has been a most familiar research interest. As the associative classification technique proved to be most efficient classifier compare to other classifiers we can found certain proposals in literature which can perform associative classification over distributed databases. Even after incremental data mining proved to be most optimized way to upgrade mined rules when new set of transaction added to database, there are lack of proposals which can perform incremental mining over distributed databases. Considering these issues the article presents incremental associative classification model over horizontally distributed databases. Experimental conducted using synthesized datasets has shown encouraging results.

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Privacy Preserving Data Mining: A Review of the State of the Art

Sharma

Ahuja

2018

Harmony Search and Nature Inspired Optimization Algorithms

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Privacy preserving associative classification on vertically partitioned databases

Cited by 7 publications

References 11 publications

Heterogeneous differential privacy for vertically partitioned databases

Heterogeneous differential privacy for vertically partitioned databases

Incremental associative classification on distributed databases

Privacy Preserving Data Mining: A Review of the State of the Art

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