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Privacy preserving distributed DBSCAN clustering

Jinfei Liu, Joshua Zhexue Huang, Jun Luo, Li Xiong
2012 Proceedings of the 2012 Joint EDBT/ICDT Workshops on - EDBT-ICDT '12  
DBSCAN is a well-known density-based clustering algorithm which offers advantages for finding clusters of arbitrary shapes compared to partitioning and hierarchical clustering methods.  ...  We first propose two protocols for privacy preserving DBSCAN clustering over horizontally and vertically partitioned data respectively and then extend them to arbitrarily partitioned data.  ...  The preliminary algorithms for computing privacy preserving distributed DBSCAN clustering are given in Section 4.  ... 
doi:10.1145/2320765.2320819 dblp:conf/edbt/LiuHLX12 fatcat:rci4iazpmjgr5eqtrsogejer5u

Quantifying the Tradeoff Between Cybersecurity and Location Privacy [article]

Dajiang Suo, M. Elena Renda, Jinhua Zhao
2021 arXiv   pre-print
privacy preservation.  ...  Further experiments reveal that DBSCAN is not scalable to large size datasets containing millions of trips, because of the large number of computations needed for clustering trips.  ...  , we recommend engineers to take three aspects used a Density-based Spatial Clustering of Applications with into account: customers’ need for privacy preservation (, Noise (DBSCAN  ... 
arXiv:2105.01262v2 fatcat:yick5s644vfo5pq4rr7svrxcdi

Privacy Preservation on Big Data using Efficient Privacy Preserving Algorithm

Johnny Antony P
2019 International Journal for Research in Applied Science and Engineering Technology  
To improve search efficiency and to provide privacy preservation for big data environment Efficient Privacy Preserving (EPP) Algorithm is used in this article.  ...  Most of the existing systems uses cryptography methods for privacy preservation.  ...  In first phase DBSCAN algorithm is used to cluster the IBM dataset. Then EPP algorithm is implemented on clustered output and finds sensitive attribute.  ... 
doi:10.22214/ijraset.2019.6048 fatcat:3iywdjtajbfj7jcxsgpml7lzre

Homomorphic Encryption Based Privacy Preservation Scheme for DBSCAN Clustering

Mingyang Wang, Wenbin Zhao, Kangda Cheng, Zhilu Wu, Jinlong Liu
2022 Electronics  
In this paper, we propose a homomorphic encryption-based privacy protection scheme for DBSCAN clustering to reduce the risk of privacy leakage during data outsourcing computation.  ...  Analysis of experimental results indicates that our proposed scheme has high clustering accuracy and can guarantee the privacy and security of the data.  ...  This paper presents a privacy-preserving scheme for homomorphic encryption-based clustering learning, which implements homomorphic DBSCAN for clustering operations on ciphertext datasets.  ... 
doi:10.3390/electronics11071046 fatcat:d6nzrllfnjao7if3ghkg5zpd24

Logistic Regression and Data Analysis on Privacy Methods on Data Streams

P Chandrakanth, Anbarasi M.S
2018 International Journal of Engineering & Technology  
Drift checking and ensemble classifier building is the basic requirements for privacy preserving data stream, which makes clear in experimentation with the support of sensitivity analysis.  ...  By using the phenomenon of autocorrelation of multivariable streams and their leveraging structures, identifying the suitable areas to add noise maximally preserves privacy and in a irreversible manner  ...  The clustering algorithms are tested on the WEKA tool. The WEKA has two important clustering mechanisms, namely DBSCAN and EM algorithms.  ... 
doi:10.14419/ijet.v7i3.12.16117 fatcat:l47mspusc5g2rixiy26rx2n5va

Decentralized collaborative TTP free approach for privacy preservation in location based services

Ajaysinh Devendrasinh Rathod, Saurabh Shah, Vivaksha J. Jariwala
2019 International Journal of Electrical and Computer Engineering (IJECE)  
There are many existing approaches in literature for privacy preserving location based services, but their solutions are at high cost or not supporting scalability.  ...  In this paper, our aim is to propose an approach along with algorithms that will help the location based services (LBS) users to provide location privacy with minimum cost and improve scalability.  ...  TTP free approach for privacy preservation in location ...  ... 
doi:10.11591/ijece.v9i6.pp5376-5385 fatcat:bit4p4rybncu7bf24pwgsbcb2u

Differential Privacy Technique for Privacy Preservation on Big Data

Johnny Antony P
2019 International Journal for Research in Applied Science and Engineering Technology  
There are many algorithms available for clustering the big data. This article focused on the density based clustering algorithms for first phase.  ...  We have many existing methods for privacy preservation but each method has its own limitation and drawbacks.  ...  DBSCAN algorithm is used for clustering the big data. Then, our proposed algorithm applied on these clustered output to extract secured knowledge.  ... 
doi:10.22214/ijraset.2019.6049 fatcat:nocjjhvhffgs3gs2h3lzxmts2y

Privacy Preserving Publication of Locations Based on Delaunay Triangulation [chapter]

Jun Luo, Jinfei Liu, Li Xiong
2014 Lecture Notes in Computer Science  
The pervasive usage of LBS (Location Based Services) has caused serious risk of personal privacy. In order to preserve the privacy of locations,  ...  Utility of Privacy Preserving DBSCAN. For DBSCAN, the utility P recision DBSCAN is defined as the same as P recision K−means since they are both clustering algorithms.  ...  Utility Results The definition of utility varies for different privacy preserving data mining algorithms.  ... 
doi:10.1007/978-3-319-06608-0_49 fatcat:ljqrhcypcvhhtl2mwkxlvdd24a

"From Perturbation Data, Regenerate of Data in Matlab"

Fehreen Hasan, Niranjan Singh
2011 International Journal of Computer Applications  
As the tool for the algorithm implementations we chose the "language of choice in industrial world" -MATLAB.  ...  We are considering an ensemble clustering method to reconstruct the initial data distribution.  ...  For the given dataset we considered two clustering techniques: kmeans method and DBSCAN algorithm.  ... 
doi:10.5120/1898-2529 fatcat:6xbfju2lenajddv5frvu7hdtka

SoK: Efficient Privacy-preserving Clustering

Aditya Hegde, Helen Möllering, Thomas Schneider, Hossein Yalame
2021 Proceedings on Privacy Enhancing Technologies  
This calls for efficient privacy-preserving clustering. In this work, we systematically analyze the state-of-the-art in privacy-preserving clustering.  ...  We compare them, assess their limitations for a practical use in real-world applications, and conclude with open challenges.  ...  ACKNOWLEDGEMENTS We thank Oliver Schick for his support with implementing PCA/OPT [19] .  ... 
doi:10.2478/popets-2021-0068 fatcat:sb2ttfjfojb45lkudipt5tu4fq

Privacy-Preserving Clustering: A New Approach Based on Invariant Order Encryption

Mihail-Iulian Pleșa, Cezar Pleșca
2020 Journal of Military Technology  
We focus on two unsupervised machine learning algorithms used for clustering: K-Means and DBSCAN.  ...  Overall, from an experimental point of view, we can conclude that order-preserving encryption is a suitable solution to the problem of input privacy of the clustering algorithm.  ... 
doi:10.32754/jmt.2020.2.10 fatcat:qxr4ds7j3zditafyjxphtwmunq

Multi-Agent Architecture for Point of Interest Detection and Recommendation

Claudia Cavallaro, Gabriella Verga, Emiliano Tramontana, Orazio Muscato
2019 Workshop From Objects to Agents  
Indeed, for POIs, which we determine using a DBSCAN algorithm, we take into account the time slots when the users visited them, to offer a more advanced service.  ...  Finally, the approach was designed to preserve the privacy of users, i.e. it does not reveal the position of users.  ...  The parameter Eps defines the radius of neighbourhood around For our data, the clustering algorithm DBSCAN has determined clusters for all SPs.  ... 
dblp:conf/woa/CavallaroVTM19 fatcat:qige62dbgzcrrccki6tskv2lsm

Encryption based Privacy Preservation on Big Data using Dynamic Data Encryption Strategy

Johnny Antony P
2019 International Journal for Research in Applied Science and Engineering Technology  
There are many algorithms available for clustering the big data. This section focus on the density based clustering algorithms and its related works.  ...  This article represents a concern about data privacy and suggests a novel data encryption approach known as Dynamic Data Encryption Strategy (DDES).  ...  The proposed approach, DDES, was used with DBSCAN algorithm to maximize the efficiency of privacy protections.  ... 
doi:10.22214/ijraset.2019.6047 fatcat:rthpdddtejfphp3oyef27wnzuy

Density Peaks Clustering with Differential Privacy

Shengna Guo, Xiaofeng Meng
2017 Conference on Innovative Data Systems Research  
Density peaks clustering (DPC) is a well-known densitybased clustering algorithm which finds clusters of arbitrary shapes.  ...  In this paper, we provide a density peaks clustering privacy protection(DPCP) model to obtain the clustering results without revealing the data via differential privacy protection.  ...  For the DBSCAN that is the classical density-based clustering algorithm. There are several privacy-preserving algorithms.  ... 
dblp:conf/cidr/GuoM17 fatcat:vgfu7tdkrbbe3gzicgjnarki7e

Critical Analysis of Density-based Spatial Clustering of Applications with Noise (DBSCAN) Techniques

Said Akbar, M.N.A. Khan
2014 International Journal of Database Theory and Application  
There are two parameters required for DBSCAN algorithm: one is ɛ (Eps) and the second is MinPts, which is the lowest number of points needed to form a cluster.  ...  The basic idea behind such algorithm for finding clusters is that for every point in the cluster the neighbour points of a specified radius Eps will be consist of the lowest number of points (MinPts).  ...  [11 ] Privacy preserving algorithms Clustering over vertically, horizontally and arbitrary data The algorithm provides security and can partition data horizontally and vertically Lack  ... 
doi:10.14257/ijdta.2014.7.5.02 fatcat:wcxu5vcjqvepfhpoyycxfr6mr4
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