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Privacy Preserving Clustering [chapter]

Somesh Jha, Luis Kruger, Patrick McDaniel
2005 Lecture Notes in Computer Science  
The crucial step in our privacy-preserving k-means is privacy-preserving computation of cluster means.  ...  In this paper, we present the design and analysis of a privacy-preserving k-means clustering algorithm, where only the cluster means at the various steps of the algorithm are revealed to the participating  ...  Privacy-preserving k-means In order, to create a privacy-preserving version of k-means that does not use a TTP we have to devise a privacy-preserving protocol to compute the cluster means.  ... 
doi:10.1007/11555827_23 fatcat:vgo23ozkbzeydlaqk7i5zy4fu4

Importance of Data Standardization in Privacy-Preserving K-Means Clustering [chapter]

Chunhua Su, Justin Zhan, Kouichi Sakurai
2009 Lecture Notes in Computer Science  
Privacy-preserving k-means clustering assumes that there are at least two parties in the secure interactive computation.  ...  Also, we provide a solution for the secure data standardization in the privacypreserving k-means clustering.  ...  In this paper, we study data variable standardization problems in the privacy preserving k-means clustering problem using the secure approximation algorithms.  ... 
doi:10.1007/978-3-642-04205-8_23 fatcat:7xjhsamhavhgnjy7oxotibgjpa

Differentially Private Clustering in High-Dimensional Euclidean Spaces

Maria-Florina Balcan, Travis Dick, Yingyu Liang, Wenlong Mou, Hongyang Zhang
2017 International Conference on Machine Learning  
In this work, we give differentially private and efficient algorithms achieving strong guarantees for k-means and k-median clustering when d = Ω(polylog(n)).  ...  We study the problem of clustering sensitive data while preserving the privacy of individuals represented in the dataset, which has broad applications in practical machine learning and data analysis tasks  ...  of all k centers while still preserving differential privacy.  ... 
dblp:conf/icml/BalcanDLMZ17 fatcat:57fau6vqcfg6rnjam6cvrfogg4

Differential identifiability clustering algorithms for big data analysis

Tao Shang, Zheng Zhao, Xujie Ren, Jianwei Liu
2021 Science China Information Sciences  
Individual privacy preservation has become an important issue with the development of big data technology.  ...  The experimental results show that both DI k-means and DI k-prototypes algorithms satisfy differential identifiability.  ...  By means of Algorithm 1, the database D can release the approximate value of cluster centers to achieve privacy preservation.  ... 
doi:10.1007/s11432-020-2910-1 fatcat:4odlynwnejapjoxgebuhwmrrea

A Random Matrix Approach to Differential Privacy and Structure Preserved Social Network Graph Publishing [article]

Faraz Ahmed, Rong Jin, Alex X. Liu
2013 arXiv   pre-print
We show that as compared to existing approaches for differential private approximation of eigenvectors, our approach is computationally efficient, preserves the utility and satisfies differential privacy  ...  Among the various privacy preserving models, Differential privacy provides the strongest privacy guarantees.  ...  Since spectral clustering employs kmeans, each set Ci can have different cluster distributions.  ... 
arXiv:1307.0475v1 fatcat:q5zb2qh2prcznkb4ldwuss2oy4

Privacy-Preserving Distributed Clustering for Electrical Load Profiling [article]

Mengshuo Jia, Yi Wang, Chen Shen, Gabriela Hug
2020 arXiv   pre-print
Using the proposed framework, we modify several commonly used clustering methods, including k-means, fuzzy C-means, and Gaussian mixture model, to provide privacy-preserving distributed clustering methods  ...  The privacy and complexity of the proposed privacy-preserving distributed clustering framework are analyzed.  ...  PRIVACY-PRESERVING DISTRIBUTED CLUSTERING FRAMEWORK This section describes the privacy-preserving distributed clustering framework for k-means, FCA, and GMM incorporating the proposed PP-AAC algorithm.  ... 
arXiv:2002.12769v1 fatcat:34yr53nrfvdylgfgn2ao5clpgy

New privacy preserving clustering methods for secure multiparty computation

Hirofumi Miyajima, Noritaka Shigei, Hiromi Miyajima, Yohtaro Miyanishi, Shinji Kitagami, Norio Shiratori
2016 Artificial intelligence research  
In this paper, we propose clustering methods such as k-means and NG for SMC and show the effectiveness in numerical simulation.  ...  Many researches on privacy preserving data mining have been done.  ...  PRIVACY PRESERVING k-MEANS AND NG Proposed k-means method for SMC A system consisting of a client and m parties is assumed (see Figure 1 ).  ... 
doi:10.5430/air.v6n1p27 fatcat:rbtapxtwlnbzpbdt4aeju3mcgq

Privacy Preservation in Online Social Networks Using Multiple-Graph-Properties-Based Clustering to Ensure k-Anonymity, l-Diversity, and t-Closeness

Rupali Gangarde, Amit Sharma, Ambika Pawar, Rahul Joshi, Sudhanshu Gonge
2021 Electronics  
Various privacy preservation methods have been introduced recently at the user and network levels, but ensuring k-anonymity and higher privacy model requirements such as l-diversity and t-closeness in  ...  Furthermore, the clusters ensure improved k-anonymization by a novel one-pass anonymization algorithm to address l-diversity and t-closeness privacy requirements.  ...  The LECC method achieved privacy preservation in the shortest time of all three techniques, as it relies on simple K-means clustering for ECC and LECC.  ... 
doi:10.3390/electronics10222877 fatcat:rd3hljolwjh5jmv67duntcnq5a

Privacy Preserving in Data Mining by Normalization

Syed Md.TariqueAhmad, Shameemul Haque, Prince Shoeb Khan
2014 International Journal of Computer Applications  
Our purpose is to use it for preserving privacy through data mining. We use K-means clustering to validate the proposed approach and validate for accuracy.  ...  and K-mean clustering techniques in our proposed system.  ... 
doi:10.5120/16797-6509 fatcat:gixmtn34djf63bn7rsqm653674

A Proposed Technique for Privacy Preservation by Anonymization Method Accomplishing Concept of K-Means Clustering and DES

Priyanka Pachauri
2017 International Journal for Research in Applied Science and Engineering Technology  
We discuss PPDM by Anonymization Method in which we use K-means clustering in order to divide the given data and DES algorithm for encryption of data in order to prevent sensitive data from attacker.  ...  Privacy-Preserving Data Mining also an essential branch of the data mining and an exciting topic in privacy preservation has gain particular attention in current years.  ...  To preserve privacy, model of k-anonymity has been proposed by the Sweeney [2] which achieve k anonymity by means of generalization and the Suppression , K-anonymity, it is difficult for an imposter  ... 
doi:10.22214/ijraset.2017.8226 fatcat:zo5nq6zt5veazimmcic7bh7j7q

Privacy Preserving Clustering on Distorted data

Thanveer Jahan
2012 IOSR Journal of Computer Engineering  
The data mining utility k-means clustering is used on these distorted datasets. Our experimental results use a real world dataset.  ...  In designing various security and privacy related data mining applications, privacy preserving has become a major concern.  ...  Thus, a complete privacy can be obtained in k-means cluster analysis and is also proved in privacy metrics. V.  ... 
doi:10.9790/0661-0522529 fatcat:ya6li4lftbbttlmf6flhnhon7u

SVDC: Preserving Privacy in Clustering using Singular Value Decomposition

Rajavel Maheswari, Karuppuswamy Duraiswamy
2008 Journal of Information Privacy and Security  
Our proposed method Clustering Singular Value Decomposition (CLUST-SVD) distorts only confidential numerical attributes to meet privacy requirements, while preserving general features for k-means clustering  ...  We focus primarily on privacy preserving data clustering.  ...  We have presented a Singular Value Decomposition method for data distortion to achieve privacy-preserving in data mining applications. We focus primarily on privacy preserving k-means clustering.  ... 
doi:10.1080/2333696x.2008.10855839 fatcat:rho6k53mufeplmfu4uouucksai

Grouping of Distributed Data at Multiple Sites by Sustaining the Privacy of Data

Sumana M, Hareesha K S
2020 Procedia Computer Science  
The proposed work discusses a privacy preserving clustering method to group identical data. One of the major challenges while modeling is to compute a representative for each cluster.  ...  The proposed work discusses a privacy preserving clustering method to group identical data. One of the major challenges while modeling is to compute a representative for each cluster.  ...  It is observed the SSE for the proposed privacy preserving k-means clustering approach is lesser than the base clustering method used for privacy preserving clustering.  ... 
doi:10.1016/j.procs.2020.04.181 fatcat:pdcv2r4e25gjpnvas3m2c6vzpq

Privacy Preserving Data Mining Based on Geometrical Data Transformation Method (GDTM) and K-Means Clustering Algorithm

Nur Athirah Jamadi, Maheyzah Md Siraj, Mazura Mat Din, Hazinah Kutty Mammy, Norafida Ithnin
2018 International Journal of Innovative Computing  
We also propose an improved PPDM that applying Geometrical Data Transformation Method (GDTM) and K-Means Clustering Algorithm for optimum accuracy of mining and zero data loss while preserving the privacy  ...  Such problems motivate the research in Privacy Preserving Data Mining (PPDM) and it became one of the newest trends.  ...  A work focusing on privacy preserving in K-means clustering by clustering rotation is proposed by [19] . Based on this, data perturbation is employed. The dataset is clustered by K-means clustering.  ... 
doi:10.11113/ijic.v8n2.174 fatcat:3icxbhdwxrgslcokgf5pbkqb44

Privacy-Preserving Clustering with High Accuracy and Low Time Complexity [chapter]

Yingjie Cui, W. K. Wong, David W. Cheung
2009 Lecture Notes in Computer Science  
This paper proposes an efficient solution with high accuracy to the problem of privacy-preserving clustering.  ...  In our research, we focus on the data perturbation approach, and propose an algorithm of linear time complexity based on 1d clustering to perturb the data.  ...  In privacy-preserving clustering, a third party data miner requires computing k-means clustering on DB.  ... 
doi:10.1007/978-3-642-00887-0_40 fatcat:r5niunkbnza6hmbxeokc6tjety
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