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Hybrid Perturbation Technique using Feature Selection Method for Privacy Preservation in Data Mining

Praveena Priyadarsini, M. L. Valarmathi, S. Sivakumari
2012 International Journal of Computer Applications  
Privacy-preserving in data mining refers to the area of data mining that seeks to safeguard sensitive information from unsolicited or unsanctioned disclosure and hence protecting individual data records  ...  The data distortion is measured using maintenance of Rank of Features (CK) between the original and perturb datasets.  ...  Peng peng Lin et.al in their work have explored the use of feature selection techniques for privacy preservation purpose.  ... 
doi:10.5120/9257-3427 fatcat:2aq72vi3svf3xjik6tpbca66sq

On Feature Selection Stability and Privacy Preserving Data Mining: A Data Perspective

Mohana Chelvan P
2020 International Journal of Advanced Trends in Computer Science and Engineering  
Besides, the privacy preserving ruffling associates stresses the stability of the selection of features and data utility.  ...  Picking proper privacy-preserving data mining technique with significant privacy-preserving ruffling to enhance feature selection stability alongside the greater privacypreservation and data utility is  ...  The privacy preserving data mining algorithms should, therefore, preserve the privacy of the citizenry, and moreover, should have greater feature selection stability as well as accuracy.  ... 
doi:10.30534/ijatcse/2020/50922020 fatcat:7nhnoehbzbaopkiqahdkodjhmi

Attribute Segregation based on Feature Ranking Framework for Privacy Preserving Data Mining

R. Praveena Priyadarsini, M. L. Valarmathi, S. Sivakumari
2015 Indian Journal of Science and Technology  
Attributes in macro-data have to be segregating based on their sensitivity for privacy preservation purposes.  ...  The level-1 perturbed dataset is further perturbed by applying SLP algorithm to form level-2 and level-3 privacy preserved datasets.  ...  The use of feature selection techniques for privacy preservation was proposed by Peng Peng Lin 9 , where Sparsified Singular value decomposition is used for data distortion and filter based feature selection  ... 
doi:10.17485/ijst/2015/v8i17/77584 fatcat:3h4b4g6zjzdihmrcmr5fqldb6y

NetFense: Adversarial Defenses against Privacy Attacks on Neural Networks for Graph Data

I-Chung Hsieh, Cheng-Te Li
2021 IEEE Transactions on Knowledge and Data Engineering  
Extensive studies also bring several insights, such as the flexibility of NetFense, preserving local neighborhoods in data unnoticeability, and better privacy protection for high-degree nodes.  ...  Recent advances in protecting node privacy on graph data and attacking graph neural networks (GNNs) gain much attention. The eye does not bring these two essential tasks together yet.  ...  (i.e., data utility) and privacy preservation discussed in Sec. 4.2.  ... 
doi:10.1109/tkde.2021.3087515 fatcat:paojqkcf2rgvzagtxl5wiezi3y

The Effects of Privacy Preserving Data Publishing based on Overlapped Slicing on Feature Selection Stability and Accuracy

Mohana Chelvan P, Perumal K
2020 International Journal of Advanced Computer Science and Applications  
As feature selection stability is datadriven, the impacts of privacy preserving data publishing based on overlapped slicing on feature selection stability and accuracy is investigated in this paper.  ...  Feature selection stability is data centric and hence modifications of a dataset for privacy preservation affects feature selection stability along with data utility.  ...  This paper is connected with the effect on data utility along with feature selection stability in data mining by perturbation of dataset for the privacy preserving data publishing methods particularly  ... 
doi:10.14569/ijacsa.2020.0111220 fatcat:7tripxedojayln3jlfrjmew4vu

An Improved Rotation-Based Privacy Preserving Classification in Web Mining Using Naïve Bayes Classifier

Subramanian SANGEETHA MARIAMMAL, Ashok KAVITHAMANI
2020 Studies in Informatics and Control  
The data stream can be secured and classified by privacy preserving techniques such as perturbation, cryptography and machine learning techniques, etc.  ...  Recently, the privacy and security of big data has become an important challenge, which requires the privacy preserving data mining techniques to maintain the trade-off between the data utility and privacy  ...  Jahan et al. (2012) have proposed the data perturbation privacy preserving approach using the feature selection method to preserve the sensitive data in the database.  ... 
doi:10.24846/v30i4y202004 fatcat:bth7sdt6rjg2tigp7xboclec2e

PPaaS: Privacy Preservation as a Service [article]

Pathum Chamikara Mahawaga Arachchige, Peter Bertok, Ibrahim Khalil, Dongxi Liu, Seyit Camtepe
2020 arXiv   pre-print
The proposed method employs selective privacy preservation via data perturbation and looks at different dynamics that can influence the quality of the privacy preservation of a dataset.  ...  PPaaS includes pools of data perturbation methods, and for each application and the input dataset, PPaaS selects the most suitable data perturbation approach after rigorous evaluation.  ...  PPaaS quantifies privacy in terms of the variance of the difference between the input data and perturbed data (V ar(P )).  ... 
arXiv:2007.02013v2 fatcat:3dwye765zrar7mwgg7xhhdebbu

Improving the Performance of Various Privacy Preserving Databases using Hybrid Geometric Data Perturbation Classification Model

Sk. Mohammed Gouse, G.Krishna Mohan
2020 International Journal of Advanced Computer Science and Applications  
In this work, a new data transformation approach, hybrid geometrical perturbation approach and hybrid boosting classifier are proposed in order to enhance the overall efficiency of the model on the privacy  ...  In order to improve the privacy, accuracy and runtime of the traditional privacy preserving models, a hybrid perturbation based privacy preserving classification model is proposed on the multiple databases  ...  These ranking methods select the top ‗k' features based on highest rank and eliminate those having lower feature ranks.  ... 
doi:10.14569/ijacsa.2020.0111034 fatcat:ckipn7ikhvhrtc35rggyhe5yvi

Differential Privacy-enabled Federated Learning for Sensitive Health Data [article]

Olivia Choudhury, Aris Gkoulalas-Divanis, Theodoros Salonidis, Issa Sylla, Yoonyoung Park, Grace Hsu, Amar Das
2020 arXiv   pre-print
We demonstrate the feasibility and effectiveness of the federated learning framework in offering an elevated level of privacy and maintaining utility of the global model.  ...  sensitive data, resource constraints for transferring and integrating data from multiple sites, and risk of a single point of failure.  ...  As a recent advancement in privacy-preserving FL, the authors in [19] adopted output perturbation or sensitivity method.  ... 
arXiv:1910.02578v3 fatcat:7okfkjznyvb6fdway4attytbxe

A Hybrid Clustering Approach and Random Rotation Perturbation (RRP) for Privacy Preserving Data Mining

Sivakumar Kaliappan, Sathyabama Institute of Science and Technology
2018 International Journal of Intelligent Engineering and Systems  
As privacy preserving data mining grants, sharing and exchanging of privacy susceptible data for analysis, it has exploited increasingly popular.  ...  These perturbed values are then stored in the public cloud and the key parameters for randomizing and the clustering is stored in the private cloud.  ...  Random rotation perturbation for privacy preserving Data perturbation is an imperative system for privacy-preserving the data.  ... 
doi:10.22266/ijies2018.1231.17 fatcat:l3h22vrlffelxfltd6nlyky3ly

Comparative Study on Perturbation Techniques in Privacy Preserving Data Mining on Two Numeric Datasets

Desmond Ko Khang Siang, Siti Hajar Othman, Raja Zahilah Raja Mohd Radzi
2018 International Journal of Innovative Computing  
There are several techniques available in PPDM and each of the techniques has its' own benefits and drawbacks. In this research, perturbation technique is selected as privacy preserving technique.  ...  In this research, three perturbation techniques are selected which are additive noise, data swapping and resample.  ...  Selected Data Mining Techniques After implementation of privacy preserving techniques, data mining such as naïve bayes and support vector machines are implemented on perturbed dataset respectively. 1  ... 
doi:10.11113/ijic.v8n1.161 fatcat:eltb2u3f7ba5bf5xme6d7anvuy

Utility-aware Privacy-preserving Data Releasing [article]

Di Zhuang, J. Morris Chang
2020 arXiv   pre-print
In this work, we propose a two-step perturbation-based utility-aware privacy-preserving data releasing framework.  ...  In the big data era, more and more cloud-based data-driven applications are developed that leverage individual data to provide certain valuable services (the utilities).  ...  each coarse-grain-perturbed data to a fine-grain-perturbed data that belongs to a randomly selected privacy target class (the data owner's secret parameter).  ... 
arXiv:2005.04369v1 fatcat:xijkzwunlnanzkbex7m5pd3g7i

Multi-Level Trust Privacy Preserving Data Mining to Enhance Data Security and Prevent Leakage of the Sensitive Data

Bourvil, Levi
2017 Bonfring International Journal of Industrial Engineering and Management Science  
The major complication in privacy-sensitive domain is solved through the development of the Multi-Level Trust Privacy Preserving Data Mining (MLT-PPDM) where multiple differently perturbed copies of the  ...  Privacy Preserving Data Mining (PPDM) is commonly utilized for the purpose of extracting related knowledge from large amount of data and simultaneously safeguard the sensitive information from the data  ...  Data Perturbation is a popular technique in PPDM and perturbation-based PPDM approach introduces random perturbation to individual values to preserve privacy before data is published.  ... 
doi:10.9756/bijiems.8327 fatcat:uf6u4mdofrgnxejzki5ihjuic4

Locally Differentially Private Naive Bayes Classification [article]

Emre Yilmaz, Mohammad Al-Rubaie, J. Morris Chang
2019 arXiv   pre-print
Individuals send their perturbed inputs that keep the relationship between the feature values and class labels. The data aggregator estimates all probabilities needed by the Naive Bayes classifier.  ...  We propose solutions for both discrete and continuous data.  ...  Since the training data must be collected from individuals by preserving privacy, we utilize LDP frequency and statistics estimation methods for collecting perturbed data from individuals and estimating  ... 
arXiv:1905.01039v1 fatcat:rszzw7vb6nf2rc2z6nq43najdm

Design and Implementation of Privacy-preserving Recommendation System Based on MASK

Yonghong Xie, Aziguli Wulamu, Xiaojing Hu, Xiaojie Zhu
2014 Journal of Software  
First, disrupt original data to form new privacy-preserving data according to a certain probability. Second, conduct data mining with the new privacy-preserving data in recommendation subsystem.  ...  A privacy-preserving recommendation system based on MASK is designed to address it. The system consists of two parts: privacy-preserving subsystem and recommendation subsystem.  ...  As previously mentioned, such chosen parameter in privacy-preserving subsystem can make private data been protected as highly as possible and the new data obtained after data perturbation as similar as  ... 
doi:10.4304/jsw.9.10.2607-2613 fatcat:yj4gnjcnure63nfc4qbfjrz2pm
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