Hiding Association Rules by Using Confidence and Support

Dasseni, Elena; Verykios, Vassilios S.; Elmagarmid, Ahmed K.; Bertino, Elisa

doi:10.1007/3-540-45496-9_27

Cited by 255 publications

(198 citation statements)

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“…We set the confidence threshold to 0.01 and this might have affected the results. While this is a low threshold, it results in a higher recall (Dasseni et al 2001) (i.e., identified a larger set of frequently co-changing classes). We further conducted a manual check on the returned association rules in the smaller projects to ensure that class pairs returned by the arules tool actually co-changed and to validate its accuracy.…”

Section: External Validity This Paper Presents the Results Of An Empimentioning

confidence: 99%

An empirical study on the interplay between semantic coupling and co-change of software classes

2017

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Software systems continuously evolve to accommodate new features and interoperability relationships between artifacts point to increasingly relevant software change impacts. During maintenance, developers must ensure that related entities are updated to be consistent with these changes. Studies in the static change impact analysis domain have identified that a combination of source code and lexical information outperforms using each one when adopted independently. However, the extraction of lexical information and the measure of how loosely or closely related two software artifacts are, considering the semantic information embedded in their comments and identifiers has been carried out using somewhat complex information retrieval (IR) techniques. The interplay between software semantic and change relationship strengths has also not been extensively studied. This work aims to fill both gaps by comparing the effectiveness of measuring semantic coupling of OO software classes using (i) simple identifier based techniques and (ii) the word corpora of the entire classes in a software system. Afterwards, we empirically investigate the interplay between semantic and change coupling. The empirical results show that: (1) identifier based methods have more computational efficiency but cannot always be used interchangeably with corpora-based methods of computing semantic coupling of classes and (2) there is no correlation between semantic and change coupling. Furthermore we found that (3) there is a directional relationship between the two, as over 70% of the semantic dependencies are also linked by change coupling but not vice versa.

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Section: External Validity This Paper Presents the Results Of An Empimentioning

confidence: 99%

An empirical study on the interplay between semantic coupling and co-change of software classes

2017

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“…The utility of the approach was defined by the number of non-sensitive rules whose support was also lowered by using such an approach. This approach was extended in [24] in which both support and confidence of the appropriate rules could be lowered. In this case, 0-values in the transactional database could also change to 1-values.…”

Section: Knowledge Hidingmentioning

confidence: 99%

Survey paper on privacy preserving data mining.

Swaminathan¹,

Kumar.²

2016

IJAR

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“…Besides these categorizations, widely known classification is done according to the nature of the base algorithm and following classes appear; heuristic based approaches, border based approaches, exact approaches, reconstruction based approaches and cryptography based approaches. Heuristic Based Approaches: Hiding problem is generalized as to consider the hiding of both sensitive frequent itemsets and sensitive association rules in (Dasseni et al, 2001). The authors propose three single rule heuristic hiding algorithms that are based on the reduction of either the support or the confidence of the sensitive rules, but not both.…”

Section: Related Workmentioning

confidence: 99%

Full-Exact Approach for Frequent Itemset Hiding

Ergenc

Ayav

2015

International Journal of Data Warehousing and Mining

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This paper proposes a novel, exact approach that relies on integer programming for association rule hiding. A large panorama of solutions exists for the complex problem of itemset hiding: from practical heuristic approaches to more accurate exact approaches. Exact approaches provide better solutions while suffering from the lack of performance and existing exact approaches still augment their methods with heuristics to make the problem solvable. In this case, the solution may not be optimum. This work present a full-exact method, without any need for heuristics. Extensive tests are conducted on 10 real datasets to analyze distance and information loss performances of the algorithm in comparison to a former similar algorithm. Since the approach provides the optimum solution to the problem, it should be considered as a reference method.

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Hiding Association Rules by Using Confidence and Support

Cited by 255 publications

References 4 publications

An empirical study on the interplay between semantic coupling and co-change of software classes

An empirical study on the interplay between semantic coupling and co-change of software classes

Survey paper on privacy preserving data mining.

Full-Exact Approach for Frequent Itemset Hiding

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