A Robust Privacy Preserving of Multiple and Binary Attribute by Using Super Modularity with Perturbation

2018 ◽  
pp. 121-132
Author(s):  
Priya Ranjan ◽  
Raj Kumar Paul
Keyword(s):  
Data Mining ◽  
Data Security ◽  
High Performance ◽  
Perturbation Technique ◽  
Threshold Value ◽  
Privacy Preserving ◽  
Digital Data ◽  
Sensitive Information ◽  
Data Mining Algorithms ◽  
Mining Algorithms

With the increase of digital data on servers different approach of data mining is applied for the retrieval of interesting information in decision making. A major social concern of data mining is the issue of privacy and data security. So privacy preserving mining come in existence, as it validates those data mining algorithms that do not disclose sensitive information. This work provides privacy for sensitive rules that discriminate data on the basis of community, gender, country, etc. Rules are obtained by aprior algorithm of association rule mining. Those rules which contain sensitive item set with minimum threshold value are considered as sensitive. Perturbation technique is used for the hiding of sensitive rules. The age of large database is now a big issue. So researchers try to develop a high performance platform to efficiently secure these kind of data before publishing. Here proposed work has resolve this issue of digital data security by finding the relation between the columns of the dataset which is based on the highly relative association patterns. Here use of super modularity is also done which balance the risk and utilization of the data. Experiment is done on large dataset which have all kind of attribute for implementing proposed work features. The experiments showed that the proposed algorithms perform well on large databases. It work better as the Maximum lost pattern percentage is zero a certain value of support.

scholarly journals Classification of Privacy Preserving Data Mining Algorithms: A Review

2020 ◽  
Vol 20 (2) ◽  
pp. 36
Author(s):  
Dedi Gunawan
Keyword(s):  
Data Mining ◽  
Extraction Process ◽  
Privacy Preserving ◽  
Sensitive Information ◽  
Time Data ◽  
Privacy Preserving Data Mining ◽  
Data Mining Techniques ◽  
Data Mining Algorithms ◽  
Using Data ◽  
Mining Algorithms

Nowadays, data from various sources are gathered and stored in databases. The collection of the data does not give a significant impact unless the database owner conducts certain data analysis such as using data mining techniques to the databases. Presently, the development of data mining techniques and algorithms provides significant benefits for the information extraction process in terms of the quality, accuracy, and precision results. Realizing the fact that performing data mining tasks using some available data mining algorithms may disclose sensitive information of data subject in the databases, an action to protect privacy should be taken into account by the data owner. Therefore, privacy preserving data mining (PPDM) is becoming an emerging field of study in the data mining research group. The main purpose of PPDM is to investigate the side effects of data mining methods that originate from the penetration into the privacy of individuals and organizations. In addition, it guarantees that the data miners cannot reveal any personal sensitive information contained in a database, while at the same time data utility of a sanitized database does not significantly differ from that of the original one. In this paper, we present a wide view of current PPDM techniques by classifying them based on their taxonomy techniques to differentiate the characteristics of each approach. The review of the PPDM methods is described comprehensively to provide a profound understanding of the methods along with advantages, challenges, and future development for researchers and practitioners.

Distributed Privacy Preserving Clustering via Homomorphic Secret Sharing and Its Application to (Vertically) Partitioned Spatio-Temporal Data

Cyber Crime ◽  
2013 ◽  
pp. 395-415 ◽  
Author(s):  
Can Brochmann Yildizli ◽  
Thomas Pedersen ◽  
Yucel Saygin ◽  
Erkay Savas ◽  
Albert Levi
Keyword(s):  
Data Mining ◽  
Real World ◽  
Privacy Preserving ◽  
Secure Multiparty Computation ◽  
Multiparty Computation ◽  
Privacy Preserving Data Mining ◽  
Computational Costs ◽  
Data Mining Algorithms ◽  
Spatio Temporal ◽  
Mining Algorithms

Recent concerns about privacy issues have motivated data mining researchers to develop methods for performing data mining while preserving the privacy of individuals. One approach to develop privacy preserving data mining algorithms is secure multiparty computation, which allows for privacy preserving data mining algorithms that do not trade accuracy for privacy. However, earlier methods suffer from very high communication and computational costs, making them infeasible to use in any real world scenario. Moreover, these algorithms have strict assumptions on the involved parties, assuming involved parties will not collude with each other. In this paper, the authors propose a new secure multiparty computation based k-means clustering algorithm that is both secure and efficient enough to be used in a real world scenario. Experiments based on realistic scenarios reveal that this protocol has lower communication costs and significantly lower computational costs.

scholarly journals Privacy Preserving Data Mining

2021 ◽  
Author(s):  
Esma Ergüner Özkoç
Keyword(s):  
Data Mining ◽  
Data Privacy ◽  
Personal Data ◽  
Privacy Preserving ◽  
Privacy Preserving Data Mining ◽  
Data Mining Techniques ◽  
Data Mining Algorithms ◽  
Data Output ◽  
The Individual ◽  
Mining Algorithms

Data mining techniques provide benefits in many areas such as medicine, sports, marketing, signal processing as well as data and network security. However, although data mining techniques used in security subjects such as intrusion detection, biometric authentication, fraud and malware classification, “privacy” has become a serious problem, especially in data mining applications that involve the collection and sharing of personal data. For these reasons, the problem of protecting privacy in the context of data mining differs from traditional data privacy protection, as data mining can act as both a friend and foe. Chapter covers the previously developed privacy preserving data mining techniques in two parts: (i) techniques proposed for input data that will be subject to data mining and (ii) techniques suggested for processed data (output of the data mining algorithms). Also presents attacks against the privacy of data mining applications. The chapter conclude with a discussion of next-generation privacy-preserving data mining applications at both the individual and organizational levels.

Distributed Privacy Preserving Clustering via Homomorphic Secret Sharing and its Application to (Vertically) Partitioned Spatio-Temporal Data

2013 ◽  
pp. 45-65
Author(s):  
Can Brochmann Yildizli ◽  
Thomas Pedersen ◽  
Yucel Saygin ◽  
Erkay Savas ◽  
Albert Levi
Keyword(s):  
Data Mining ◽  
Real World ◽  
Privacy Preserving ◽  
Secure Multiparty Computation ◽  
Multiparty Computation ◽  
Privacy Preserving Data Mining ◽  
Computational Costs ◽  
Data Mining Algorithms ◽  
Spatio Temporal ◽  
Mining Algorithms

Recent concerns about privacy issues have motivated data mining researchers to develop methods for performing data mining while preserving the privacy of individuals. One approach to develop privacy preserving data mining algorithms is secure multiparty computation, which allows for privacy preserving data mining algorithms that do not trade accuracy for privacy. However, earlier methods suffer from very high communication and computational costs, making them infeasible to use in any real world scenario. Moreover, these algorithms have strict assumptions on the involved parties, assuming involved parties will not collude with each other. In this paper, the authors propose a new secure multiparty computation based k-means clustering algorithm that is both secure and efficient enough to be used in a real world scenario. Experiments based on realistic scenarios reveal that this protocol has lower communication costs and significantly lower computational costs.

Sensitive Items in Privacy Preserving — Association Rule Mining

2008 ◽  
Vol 07 (01) ◽  
pp. 31-35
Author(s):  
K. Duraiswamy ◽  
N. Maheswari
Keyword(s):  
Data Mining ◽  
Private Information ◽  
Association Rule ◽  
Association Rule Mining ◽  
Privacy Preserving ◽  
Data Input ◽  
Rule Mining ◽  
Privacy Preserving Data Mining ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Privacy-preserving has recently been proposed in response to the concerns of preserving personal or sensible information derived from data-mining algorithms. For example, through data-mining, sensible information such as private information or patterns may be inferred from non-sensible information or unclassified data. As large repositories of data contain confidential rules that must be protected before published, association rule hiding becomes one of important privacy preserving data-mining problems. There have been two types of privacy concerning data-mining. Output privacy tries to hide the mining results by minimally altering the data. Input privacy tries to manipulate the data so that the mining result is not affected or minimally affected. For some applications certain sensitive predictive rules are hidden that contain given sensitive items. To identify the sensitive items an algorithm SENSITEM is proposed. The results of the work have been given.

scholarly journals A SURVEY OF PRIVACY PRESERVING DATA MINING ALGORITHMS

2021 ◽  
Author(s):  
Abou el ela Abdou Hussein ◽  
Nermin Hamza ◽  
Ashraf A. Shahen ◽  
Hesham A. Hefny
Keyword(s):  
Data Mining ◽  
Privacy Preserving ◽  
Privacy Preserving Data Mining ◽  
Data Mining Algorithms ◽  
Mining Algorithms
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