A Privacy Preserved Data Mining Approach Based on k-Partite Graph Theory

Bhat, Tajesvi; Karthik, C.; Chandrasekaran, K.

doi:10.1016/j.procs.2015.06.049

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…The authors designed a model for privacy-preserving computation of multidimensional aggregates on data partitioned across multiple clients before it is integrated at the server side. In the work of Bhat et al [12], a graph theoretical approach based on k-partitioning of graphs, which paves way to creation of a complex decision tree classifier, organised in a prioritised hierarchy, was proposed to address problems causing a big privacy breach. Navarro-Arribas et al [13] addressed the problems of protecting sensitive data items in query logs by ensuring the anonymity of the users in the logs.…”

Section: Related Workmentioning

confidence: 99%

Theoretical Model for Detecting Sensitive Data Items of Users in Data Publication

Charles¹,

Hou²,

Eghan-Yartel³

2019

IJCA

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Developments in current information technology are leading to the increased capture and storage of information about people and their activities. This raises serious concerns about the which data items are sensitive and how to detect these sensitive data items. Data privacy has become a very important concern in data publication in this modern era. The protection of data privacy depends on exactly what needs to be kept secret, thus, sensitive data. Protecting data privacy is a complicated task that takes into consideration what needs to be kept confidential. However, current privacy modeling techniques assume sensitive data items. This paper considers the detection of sensitive data items in data publication for research purposes. We attempt to theoretically formalize a model for detecting sensitive data using a directed graph. We identify transitions that have a lot of sensitive data items published to them; critical transitions. Furthermore, the state that is most risky to the user to traverse in the graph, termed the "Risky State" was ascertained.

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Section: Related Workmentioning

confidence: 99%

Theoretical Model for Detecting Sensitive Data Items of Users in Data Publication

Charles¹,

Hou²,

Eghan-Yartel³

2019

IJCA

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“…The major concern of these schemes is to preserve the sensitive data [20,23] of parties, while they achieve valuable knowledge from the entire dataset. A major concern in DM [5,16] is the process of finding out frequent item sets, and subsequently association rules [21] are frequently exploited in numerous areas. The majority of the PPDM [9,11] schemes exploit a transformation that minimizes the convenience of the underlying data when it is applied to data-mining algorithms.…”

Section: Introductionmentioning

confidence: 99%

Opposition Intensity-Based Cuckoo Search Algorithm for Data Privacy Preservation

Shailaja

Rao

2019

Journal of Intelligent Systems

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Abstract Privacy-preserving data mining (PPDM) is a novel approach that has emerged in the market to take care of privacy issues. The intention of PPDM is to build up data-mining techniques without raising the risk of mishandling of the data exploited to generate those schemes. The conventional works include numerous techniques, most of which employ some form of transformation on the original data to guarantee privacy preservation. However, these schemes are quite multifaceted and memory intensive, thus leading to restricted exploitation of these methods. Hence, this paper intends to develop a novel PPDM technique, which involves two phases, namely, data sanitization and data restoration. Initially, the association rules are extracted from the database before proceeding with the two phases. In both the sanitization and restoration processes, key extraction plays a major role, which is selected optimally using Opposition Intensity-based Cuckoo Search Algorithm, which is the modified format of Cuckoo Search Algorithm. Here, four research issues, such as hiding failure rate, information preservation rate, and false rule generation, and degree of modification are minimized using the adopted sanitization and restoration processes.

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“…The "Got:"-fields and other data in the email can regularly recognize the sender, averting unknown cores. [5,6,7]…”

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confidence: 99%

A Novel Method for Detecting Spam Email using KNN Classification with Spearman Correlation as Distance Measure

Sharma¹,

Suryawanshi²

2016

IJCA

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A Privacy Preserved Data Mining Approach Based on k-Partite Graph Theory

Cited by 8 publications

References 7 publications

Theoretical Model for Detecting Sensitive Data Items of Users in Data Publication

Theoretical Model for Detecting Sensitive Data Items of Users in Data Publication

Opposition Intensity-Based Cuckoo Search Algorithm for Data Privacy Preservation

A Novel Method for Detecting Spam Email using KNN Classification with Spearman Correlation as Distance Measure

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