Anonymizing Social Network Using Bipartite Graph

Lan, Lihui; Ju, Shiguang; Jin, Hua

doi:10.1109/iccis.2010.245

Cited by 16 publications

(4 citation statements)

References 6 publications

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“…Bipartite graph disebut juga bigraph yang mana dalam teori graph merupakan graph yang terdiri dari dua kelas node berbeda dimana node yang sama tidak dapat terhubung secara langsung [1] [2]. Karakteristik tersebut membuat bipartite graph tidak memiliki odd cycle [1].…”

Section: Pendahuluanunclassified

Traversal Struktur Data Bipartite Graph dalam Graph Database menggunakan Depth-First Search

Setialana

Ardiansyah

2021

ELINVO

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Bipartite graph merupakan satu bentuk graph yang dapat digunakan dalam membentuk sebuah strukur data yang saling berelasi namun memiliki karakteristik dengan dua jenis node yang berbeda seperti data hubungan keluarga atau data pohon keluarga. Dalam menyimpan struktur data bipartite graph ke sebuah database dapat digunakan graph database dengan konsep dimana node saling saling terhubung dengan node lainnya. Bipartite graph yang dikombinasikan dengan graph database menghasilkan solusi yang tepat dalam menyimpan data berelasi dengan dua jenis node yang berbeda. Namun dalam solusi tersebut menimbulkan permasalahan baru mengenai pencarian atau penelusuran (traversal) terhadap data yang terdapat dalam struktur data tersebut. Tujuan dari penelitian ini adalah mengembangkan algoritma yang dapat digunakan dalam penelusuran (traversal) data pada struktur bipartite graph dalam graph database. Algoritma yang dikembangkan adalah terapan dari algoritma depth-first search (DFS) yang telah dimodifikasi sehingga dapat digunakan dalam penelusuran (traversal) data pada bipartite graph dalam graph database. Hasil pengujian terhadap algoritma tersebut yang telah diimplementasikan ke dalam program terhadap data yang ada pada satu garis ikatan keluarga besar yaitu keluarga “George Washington /CASSIDY/” pada 30 kali percobaan dengan satuan waktu nanosecond yaitu 10-9 detik menunjukkan waktu maksimal yang didapat sebesar 42831 nanosecond, waktu minimal 5150 nanosecond, dan didapat rata-rata waktu penelusuran sebesar 9407,93 nanosecond.

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Section: Pendahuluanunclassified

Traversal Struktur Data Bipartite Graph dalam Graph Database menggunakan Depth-First Search

Setialana

Ardiansyah

2021

ELINVO

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“…Yuan et al [21] defined k-degree L-diversity anonymity model for the protection of structural information and sensitive labels of people. Various other techniques [22][23][24][25][26][27][28][29][30][31][32][33] have also been developed to anonymize the data. It has been observed from the literature at our disposal that mainly three anonymization techniques have been used for anonymization of micro-data as well as social networks data namely K-anonymity, L-diversity and T-closeness.…”

Section: Table1 Brief Of Anonymization Using K-anonymity Author Briementioning

confidence: 99%

An Approach of Privacy Preserving based Publishing in Twitter

Singh

Bansal

Sofat

2014

Proceedings of the 7th International Conference on Security of Information and Networks

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With the increase in online publishing of social network data the requirement to protect confidential information related to users has become the main concern of publishers. To cater to this need many anonymization techniques like K-anonymity, L-diversity and T-closeness has been proposed by various researchers for micro-data as well as social network data. In this paper we aim to protect sensitive information of users of Twitter-second most popular social networking site. For the purpose of carrying out anonymization, a crawler has been developed to collect data of around 10K users from publicly available information. Data of around 30 users have been used to carry out the experimental work using ARX tool. All three anonymization methods: Kanonymity, L-diversity and T-closeness have been used. Performance of technique is evaluated using information gain as a metric.

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“…Most of the works are based on using Anonymization techniques (Zhou et.al(2008)) [22],Encryption based (Guha et.al (2008) [28] proposed a model using K-nearest neighborhood along with EBB algorithm for utilizing the privacy and the concepts of sub-graphs are considered by (Lan et.al(2010)) [29] and the concepts of friendship means have been focused to ensure privacy and sending the sensitive information to these links was proposed by Heathely et.al(2013) [30].…”

Section: B Related Workmentioning

confidence: 99%

A Frame Work for Preserving Privacy in Social Media using Generalized Gaussian Mixture Model

Anuradha¹,

Srinivas²,

Prasad³

2015

ijacsa

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Abstract-Social networking sites helps in developing virtual communities for people to share their thoughts, interest activities or to increase their horizon of camaraderie. Social networking sites come under few of the most frequently browsed categories websites in the world. Nevertheless Social Networking sites are also vulnerable to various problems, threats and attacks such as disclosure of information, identity thefts etc. Privacy practice in social networking sites often come into sight, as information sharing stands in conflict with the disclosure-related misuse. Face book is one such most popular and widely used Social Networking sites which have its own robust set of Privacy mechanisms. Yet they are also prone to various privacy issues and attacks. The impulse in this paper lies in proposing a novel approach for improving the privacy among the social networking sites .The article presents the issues by a novel approach based on tagging and a model based technique based on generalized Gaussian Mixture Model.

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Anonymizing Social Network Using Bipartite Graph

Cited by 16 publications

References 6 publications

Traversal Struktur Data Bipartite Graph dalam Graph Database menggunakan Depth-First Search

Traversal Struktur Data Bipartite Graph dalam Graph Database menggunakan Depth-First Search

An Approach of Privacy Preserving based Publishing in Twitter

A Frame Work for Preserving Privacy in Social Media using Generalized Gaussian Mixture Model

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