scholarly journals SW-SDF based privacy preserving data classification

2013 ◽  
Vol 4 (3) ◽  
pp. 813-820
Author(s):  
Kiran P ◽  
Kavya N. P.
Keyword(s):  
Privacy Preservation ◽  
Data Classification ◽  
Privacy Preserving ◽  
Information Loss ◽  
Personal Privacy ◽  
Privacy Preserving Data Mining ◽  
The Core ◽  
Sensitive Attribute ◽  
Mining Algorithms

The core objective of privacy preserving data mining is to preserve the confidentiality of individual even after mining. The basic advantage of personalized privacy preservation is that the information loss is very less as compared with other privacy preservation algorithms. These algorithms how ever have not been designed for specific mining algorithms. SW-SDF personalized privacy preservation uses two flags SW and SDF. SW is used for assigning a weight for the sensitive attribute and SDF for sensitive disclosure which is accepted from individual. In this paper we have designed an algorithm which uses SW-SDF personal privacy preservation for data classification. This method ensures privacy and classification of data.

scholarly journals A Clustering Approach for the l-Diversity Model in Privacy Preserving Data Mining Using Fractional Calculus-Bacterial Foraging Optimization Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Pawan R. Bhaladhare ◽  
Devesh C. Jinwala
Keyword(s):  
Data Mining ◽  
Fractional Calculus ◽  
Private Information ◽  
Privacy Preservation ◽  
Clustering Algorithms ◽  
Privacy Preserving ◽  
Information Loss ◽  
Bacterial Foraging Optimization ◽  
Privacy Preserving Data Mining ◽  
Computational Performance

In privacy preserving data mining, the l-diversity and k-anonymity models are the most widely used for preserving the sensitive private information of an individual. Out of these two, l-diversity model gives better privacy and lesser information loss as compared to the k-anonymity model. In addition, we observe that numerous clustering algorithms have been proposed in data mining, namely, k-means, PSO, ACO, and BFO. Amongst them, the BFO algorithm is more stable and faster as compared to all others except k-means. However, BFO algorithm suffers from poor convergence behavior as compared to other optimization algorithms. We also observed that the current literature lacks any approaches that apply BFO with l-diversity model to realize privacy preservation in data mining. Motivated by this observation, we propose here an approach that uses fractional calculus (FC) in the chemotaxis step of the BFO algorithm. The FC is used to boost the computational performance of the algorithm. We also evaluate our proposed FC-BFO and BFO algorithms empirically, focusing on information loss and execution time as vital metrics. The experimental evaluation shows that our proposed FC-BFO algorithm derives an optimal cluster as compared to the original BFO algorithm and existing clustering algorithms.

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 Publishing for Multiple Sensitive Attributes Based on Security Level

Information ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 166
Author(s):  
Yuelei Xiao ◽  
Haiqi Li
Keyword(s):  
Data Privacy ◽  
Privacy Preserving ◽  
Information Loss ◽  
Experimental Results ◽  
Data Publishing ◽  
Security Level ◽  
Sensitive Attribute ◽  
Data Volume ◽  
Security Levels ◽  
Privacy Preserving Data Publishing

Privacy preserving data publishing has received considerable attention for publishing useful information while preserving data privacy. The existing privacy preserving data publishing methods for multiple sensitive attributes do not consider the situation that different values of a sensitive attribute may have different sensitivity requirements. To solve this problem, we defined three security levels for different sensitive attribute values that have different sensitivity requirements, and given an L s l -diversity model for multiple sensitive attributes. Following this, we proposed three specific greed algorithms based on the maximal-bucket first (MBF), maximal single-dimension-capacity first (MSDCF) and maximal multi-dimension-capacity first (MMDCF) algorithms and the maximal security-level first (MSLF) greed policy, named as MBF based on MSLF (MBF-MSLF), MSDCF based on MSLF (MSDCF-MSLF) and MMDCF based on MSLF (MMDCF-MSLF), to implement the L s l -diversity model for multiple sensitive attributes. The experimental results show that the three algorithms can greatly reduce the information loss of the published microdata, but their runtime is only a small increase, and their information loss tends to be stable with the increasing of data volume. And they can solve the problem that the information loss of MBF, MSDCF and MMDCF increases greatly with the increasing of sensitive attribute number.

A State Decision Tree based Backtracking Algorithm for Multi-Sensitive Attribute Privacy Preserving

2016 ◽  
Vol 8 (2) ◽  
pp. 1-11
Author(s):  
Yanchao Zhang ◽  
Qing Liu ◽  
JunJun Cheng ◽  
JiJia Yang
Keyword(s):  
Decision Tree ◽  
Original Data ◽  
Privacy Preserving ◽  
Information Loss ◽  
Published Data ◽  
Sensitive Attribute

Beyond l-diversity model, an algorithm (l-BDT) based on state decision tree is proposed in this paper, which aims at protecting multi-sensitive attributes from being attacked. The algorithm considers the whole situations in equivalence partitioning for the first, prunes the decision tree according to some conditions for the second, and screens out the method with the least information loss of equivalence partitioning for the last. The analysis and experiments show that the l-BDT algorithm has the best performance in controlling the information loss. It can be ensured that the published data is the most closed towards the original data, so as to ensure that the published data is as useful as possible.

scholarly journals DATA PROCESSING THROUGH AN ADDITIVE ROTATIONAL PERTURBATION TECHNIQUE IN A SECURED ENVIRONMENT OF PPRIVACY

2021 ◽  
Vol 9 (2) ◽  
pp. 131-135
Author(s):  
G. Srinivas Reddy, Et. al.
Keyword(s):  
Data Mining ◽  
Privacy Preservation ◽  
Web Applications ◽  
Perturbation Technique ◽  
Privacy Preserving ◽  
Security And Privacy ◽  
Experimental Result ◽  
Perturbation Approach ◽  
Privacy Preserving Data Mining ◽  
Rotational Perturbation

As the usage of internet and web applications emerges faster, security and privacy of the data is the most challenging issue which we are facing, leading to the possibility of being easily damaged. Various conventional techniques are used for privacy preservation like condensation, randomization and tree structure etc., the limitations of the existing approaches are, they are not able to maintain proper balance between the data utility and privacy and it may have the problem with privacy violations. This paper presents an Additive Rotation Perturbation approach for Privacy Preserving Data Mining (PPDM). In this proposed work, various dataset from UCI Machine Learning Repository was collected and it is protected with a New Additive Rotational Perturbation Technique under Privacy Preserving Data Mining. Experimental result shows that the proposed algorithm’s strength is high for all the datasets and it is estimated using the DoV (Difference of Variance) method.

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.

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