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Data and Structural k-Anonymity in Social Networks [chapter]

Alina Campan, Traian Marius Truta
2009 Lecture Notes in Computer Science  
Our main contributions in this paper are a greedy algorithm for anonymizing a social network and a measure that quantifies the information loss in the anonymization process due to edge generalization.  ...  The advent of social network sites in the last years seems to be a trend that will likely continue.  ...  Conclusions and Future Work In this paper we studied a new anonymization approach for social network data.  ... 
doi:10.1007/978-3-642-01718-6_4 fatcat:e33koesrxzdzxfgojhwawrzjqy

Social Networking for Data Preservation: A Study

Simran Chaudhary, Sanjeev Dhawan
2017 Indian Journal of Science and Technology  
Methods/Statistical Analysis: K-anonymity, L-diversity is used for social networking. In k-anonymity privacy micro data requires that each class contains at least K records.  ...  Objectives: This paper provides an overview of social network, privacy preservation in social networking, using kanonymity and L-diversity.  ...  Vertex u into L is k-anonymous simply if there is k-1 another vertices, like for and considered as isomorphic. Social network could be k-anonymous when each vertex inclined in L as k-anonymous.  ... 
doi:10.17485/ijst/2017/v10i20/108260 fatcat:f5v6jgnkhzhqza6eb3wbrpgw6y

SACK: Anonymization of Social Networks by Clustering of K-edge-connected Subgraphs

Fatemeh HeidariSoureshjani, Arash Ghorbannia Delavar, Fatemeh Rashidi
2013 International Journal of Computer Applications  
In this paper, a method for anonymization of social networks by clustering of k-edge-connected subgraphs (SACK) is presented.  ...  Using connected subgraphs in anonymization process this method obtains better experimental results both in data quality and time.  ...  Social networks have a more complex data structure, which contains some structural data, in addition to descriptive data.  ... 
doi:10.5120/13412-1067 fatcat:3rq7j2yqb5earb6dwbpwfgfm2m

A Differentiated Anonymity Algorithm for Social Network Privacy Preservation

Yuqin Xie, Mingchun Zheng
2016 Algorithms  
Furthermore, we design and implement a differentiated k-anonymity l-diversity social network anonymity algorithm, which seeks to protect users' privacy in social networks and increase the usability of  ...  on social networks may result in nontrivial utility loss without analyzing the social network topological structure and without considering the attributes of sparse distribution.  ...  Acknowledgments: This work is partially supported by the Natural Science Foundation of China (No. 61402266), the Social Science Foundation of China (No.14BTQ049) and the Soft Science Foundation of Shandong  ... 
doi:10.3390/a9040085 fatcat:yg5knfrcczc4fg5kp4uitauh74

Preserving Communities in Anonymized Social Networks

Alina Campan, Yasmeen Alufaisan, Traian Marius Truta
2015 Transactions on Data Privacy  
To anonymize social networks we use two models, namely, k-anonymity for social networks and k-degree anonymity.  ...  In this paper we study if anonymized social networks preserve existing communities from the original social networks.  ...  Social Network Anonymity Models In this section the two anonymity models used in this paper, k-anonymity for social networks and k-degree anonymity, are presented.  ... 
dblp:journals/tdp/CampanAT15 fatcat:yyqhtoz4mjhfhit2sffimtllfy

Privacy Preserving Techniques in Social Networks Data Publishing - A Review

Amardeep Singh, Divya Bansal, Sanjeev Sofat
2014 International Journal of Computer Applications  
Tabular micro-data is anonymized using divide-and-conquer techniques whereas social network is a structure of nodes and edges, any changes in labels or edges may have an effect on the neighborhoods of  ...  [55] Proposed methods to enhance edgeperturbing anonymization on the basis of structural roles and edge betweenness in social network theory. Dataset: Polbook, Jazz 2013 Tassa et al.  ... 
doi:10.5120/15282-3880 fatcat:mc5nmhiwxzdo7fpd7jfptjiyjq

Seed-Based De-Anonymizability Quantification of Social Networks

Shouling Ji, Weiqing Li, Neil Zhenqiang Gong, Prateek Mittal, Raheem Beyah
2016 IEEE Transactions on Information Forensics and Security  
a social network, how de-anonymizable a social network is, and how many users of a social network can be successfully de-anonymized.  ...  In this paper, we implement the first comprehensive quantification of the perfect de-anonymizability and partial de-anonymizability of real-world social networks with seed information under general scenarios  ...  Our findings are expected to shed light on research questions in structural data anonymization and de-anonymization and help data owners evaluate their data vulnerability before data sharing/publishing  ... 
doi:10.1109/tifs.2016.2529591 fatcat:qlavnsptjbbhphlabjwii7mqwi

Applying l-Diversity in anonymizing collaborative social network [article]

Ajay Prasad, G.K.Panda, A. Mitra, Arjun Singh, Deepak Gour
2010 arXiv   pre-print
Preserving privacy in social networks against neighborhood attacks is an initiation which uses the definition of privacy called k-anonymity. k-anonymous social network still may leak privacy under the  ...  In this paper, we take a step further on preserving privacy in collaborative social network data with algorithms and analyze the effect on the utility of the data for social network analysis.  ...  Preserving privacy in Social Networks using k-anonymity The algorithm suggested by Bin and Jian [15] to anonymize a social network describes two basic steps as summarized below.  ... 
arXiv:1007.0292v1 fatcat:wuvcjw3mkjayzpir7udhibqq2m

Anonymized Social Networks Community Preservation

Jyothi Vadisala, Valli Kumari
2017 International Journal of Advanced Computer Science and Applications  
In this paper, we study how the k-degree and k-NMF anonymized methods preserve the existing communities of the original social networks.  ...  We conduct the experiments on real data sets and compare the performances of the two anonymized social networks for preservation of communities of the original social networks.  ...  to the unique structure of the social network data.  ... 
doi:10.14569/ijacsa.2017.080765 fatcat:c3a24yxicfh5viu23eolx36vqu

Privacy Preservation Method Based on Clustering Interference Algorithm in Social Networks

Ran Zhang, Xianping Wu
2022 Journal of Engineering Science and Technology Review  
The information loss in social network structure was analysed and compared by using the MASN (Masking Algorithm for Social Networks) MASN, SaNGreeA (Social Network Greedy Anonymization), and p-sensitive  ...  k-anonymity model.  ...  Identification of anonymity in social networks The privacy preservation strategy in the social network is divided into anonymous vertex, anonymous subgraph, data interruption, and clustering.  ... 
doi:10.25103/jestr.152.22 fatcat:pcpvweesxffipm4ax2cmoc7t6y

Multi-View Low-Rank Coding based Network Data De-anonymization

Xingping Xian, Tao Wu, Shaojie Qiao, Wei Wang, Yanbing Liu, Nan Han
2020 IEEE Access  
Owing to the dependency and complexity of network data, privacy preserving about social network data is much more challenging than the anonymization of the conventional tabular data, and the anonymization  ...  role in network structure de-anonymization.  ... 
doi:10.1109/access.2020.2995568 fatcat:4sscpvsvn5hmthywixezbspqzm

Sensitive-resisting Relation Social Network Privacy Protection Model

Han Yan
2015 International Journal of Security and Its Applications  
Campan and other scholars proposed the k-anonymous model, and this model requires there exist more than k-attribute individual which cannot be differentiated in social network.  ...  Through the data set experiment, this paper proposes new personalized model K_L, which has the high anonymous quality and can effectively protect user's privacy in the social network. 196 Copyright ⓒ 2015  ...  Proof: We summed up the problem of k-anonymity in social networks as k-diversity in social networks.  ... 
doi:10.14257/ijsia.2015.9.8.16 fatcat:k435d2zalbeinjytdn6yinpygy

Privacy Risks and Countermeasures in Publishing and Mining Social Network Data

Chiemi Watanabe, Toshiyuki Amagasa, Ling Liu
2011 Proceedings of the 7th International Conference on Collaborative Computing: Networking, Applications and Worksharing  
As interests in sharing and mining social network data continue to grow, we see a growing demand for privacy preserving social network data publishing.  ...  In this paper, we discuss privacy risks in publishing social network data and the design principles for developing countermeasures. The main contributions of this study are three folds.  ...  , and Intel ISTC grant.  ... 
doi:10.4108/icst.collaboratecom.2011.247177 dblp:conf/colcom/WatanabeAL11 fatcat:fo27obltczbu7kgsrkcfk4msuq

Identity Disclosure Protection in Dynamic Networks Using KW – Structural Diversity Anonymity

Gowthamy R, Uma P
2016 International Journal on Integrating Technology in Education  
in social networks.  ...  In K W -structural diversity anonymity technique, k is privacy level applied for users and W is an adversary monitoring time.  ...  In k w -structural diversity anonymity, k denotes the number of user in social network for applying privacy level and w denotes the tracing time period of an adversary.  ... 
doi:10.5121/ijite.2016.5103 fatcat:sq2c5lqb3vhe7bncptblk4mtp4

Privacy protection of medical data in social network

Jie Su, Yi Cao, Yuehui Chen, Yahui Liu, Jinming Song
2021 BMC Medical Informatics and Decision Making  
Our method achieved better results of privacy preservation in social network by optimizing generalization loss and structure loss.  ...  In this paper, we first analyzed the importance of the key attributes of medical data in the social network.  ...  A clustering approach for data and structural anonymity in social networks was also given [16] .  ... 
doi:10.1186/s12911-021-01645-0 pmid:34663276 fatcat:sn7c2lcg4rcwjeieapa6djwzg4
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