A Novel Heuristic Algorithm for Privacy Preserving of Associative Classification

Harnsamut, Nattapon; Natwichai, Juggapong

doi:10.1007/978-3-540-89197-0_27

Cited by 11 publications

(7 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The suggested approach was taken into consideration by the authors for use in a number of fields of pattern mining, including maximum and closed, differential privacy, and anonymization. In an effort to stop the leaking of sensitive information, [18] created the FPUTT method, which is built on a tree structure and performs database perturbation. The sensitive item sets in this case are limited to the HUI mining technique for the specified transaction database.…”

Section: Literature Reviewmentioning

confidence: 99%

Techniques for Secure Data Contribution and Retrieval in Social Networks Using Effective Privacy-Preserving Data Mining

Reddy¹,

Hussain²,

Sagar³

et al. 2022

IJRASET

View full text Add to dashboard Cite

The major area of data-mining methods that focuses on protecting personal information from unauthorised or unsolicited exposure is called Privacy Preserving Data-Mining (PPDM). The most valuable information is analysed and predicted using data-mining methods. The security of confidential information from unwanted access is at the core of PPDM abstraction. The Secure Data Contribution Retrieval Algorithm (SDCRA), Enhanced-Attribute Based Encryption (E-ABE), Level by Level Security Optimization and Content Visualization (LSOCV) algorithm, and Privacy Preserved Hadoop Environment are just a few of the proposed methods in this research work to increase privacy and security (PPHE). To address the immediate difficulties, the proposed SDCRA is first taken into consideration. Based on specifications and application compatibility, the SDCRA algorithm determines a privacy policy and sets up security. The accuracy requirements for numerous datasets may be met by this approach. Online social networks (OSNs) are presently favoured interactive medium for establishing communication, sharing, and disseminating a sizable quantity of data on human existence.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Techniques for Secure Data Contribution and Retrieval in Social Networks Using Effective Privacy-Preserving Data Mining

Reddy¹,

Hussain²,

Sagar³

et al. 2022

IJRASET

View full text Add to dashboard Cite

show abstract

“…The MCCRT requires the CCR of each attribute to determine the order for generalization. In this article, instead of maintaining the CCRs in a list‐based structure as in Harnsamut and Natwichai (), a simple hash table is implemented to improve the efficiency. The attribute identifier along with its value will form a key for the hashing function, denoted as < A j , v > .…”

Section: Data Transformation Algorithmmentioning

confidence: 99%

“…And when the effect of the size of input dataset is investigated, we fix the k value at 20, and the size of the quasi‐identifier is set at the maximum value. We also report the execution time of the list‐based MCCRT proposed by Harnsamut and Natwichai () for comparison purposes.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…Although the metric, C FCM , and the MCCRT algorithm were first proposed by Harnsamut et al () and Harnsamut and Natwichai (), respectively, in this article, we report the following additional progress: As the MCCRT requires the CCR value of each attribute to transform the given data sets. Instead of maintaining the CCRs in a list‐based structure as in Harnsamut and Natwichai (), in this article, a simple hash table is proposed for the efficiency improvement.Extensive experiment results are presented. We investigate the effect of various parameters in the problem setting using two real‐life datasets from the UCI repository.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Privacy Preservation for Associative Classification

Harnsamut

Natwichai

Sun

et al. 2014

Computational Intelligence

Self Cite

View full text Add to dashboard Cite

Privacy preservation is becoming a critical issue to data‐mining processes. In practice, a data transformation process is often needed to preserve privacy. However, data transformation would introduce a data quality issue. In this case, the impact on data quality due to the data transformation should be estimated and made clear to the user of the data transformation process. In this article, we consider the problem of k‐anonymization transformation in associative classification. The privacy preservation and data quality issues are considered in twofold. First, we propose a frequency‐based data quality metric to represent the data quality for associative classification. Second, a novel heuristic algorithm, namely minimum classification correction rate transformation, is proposed. The algorithm is guided by the classification correction rate of the given datasets. We validate our proposed metric and algorithm with University of California–Irvine repository datasets. The experiment results have shown that our proposed metric can effectively demonstrate the data quality for associative classification. The results also show that the proposed algorithm is not only efficient but also highly effective.

show abstract

“…Such classification model works on support and confidence scheme as association rules [2], but having a designate attribute as class label. As the problem of privacy preserving for data mining has proven as an NP-hard when the optimal data quality is required, we propose to study the characteristics of a proven heuristic algorithm, Minimum Classification Correction Rate Transformation (MCCRT) [4] in the data incremental scenarios theoretically. Such heuristic algorithm can transform datasets both effectively and efficiently, i.e.…”

Section: Introductionmentioning

confidence: 99%

Incremental privacy preservation for associative classification

Seisungsittisunti

Natwichai

2009

Proceedings of the ACM First International Workshop on Privacy and Anonymity for Very Large Databases

Self Cite

View full text Add to dashboard Cite

Privacy preserving has become an essential process for any data mining task. Therefore, data transformation to ensure privacy preservation is needed. In this paper, we address a problem of privacy preserving on an incremental-data scenario in which the data need to be transformed are not static, but appended all the time. Our work is based on a well-known data privacy model, i.e. k -Anonymity. Meanwhile the data mining task to be applied to the given dataset is associative classification. As the problem of privacy preserving for data mining has proven as an NP-hard, we propose to study the characteristics of a proven heuristic algorithm in the incremental scenarios theoretically. Subsequently, we propose a few observations which lead to the techniques to reduce the computational complexity for the problem setting in which the outputs remains the same. In addition, we propose a simple algorithm, which is at most as efficient as the polynomial-time heuristic algorithm in the worst case, for the problem.

show abstract

A Novel Heuristic Algorithm for Privacy Preserving of Associative Classification

Cited by 11 publications

References 8 publications

Techniques for Secure Data Contribution and Retrieval in Social Networks Using Effective Privacy-Preserving Data Mining

Techniques for Secure Data Contribution and Retrieval in Social Networks Using Effective Privacy-Preserving Data Mining

Privacy Preservation for Associative Classification

Incremental privacy preservation for associative classification

Contact Info

Product

Resources

About