Personalized anonymity in social networks data publication

Lan, Lihui; Jin, Hua; Lü, Yang

doi:10.1109/csae.2011.5953265

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…Dataset used: HEP-Th, EUmail, LiveJournal Wu et al [13] Proposed k-symmetry technique to protect privacy against re-identification using subgraph information. Dataset used: Hepth, Enron, Net-trace Lan et al [14] An algorithm called KNAP against 1-neighborhood attack has been developed for publishing social networks data. Dataset used: Synthetic data Truta et al [15] Studied how well several structural properties of a social network are preserved through an anonymization process.…”

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

confidence: 99%