Earticle

초록

영어

In the last decade online social networks has gained remarkable attention. Facebook or Google+, are example social network services which allow people to create online profiles and share personal information with their friends. These networks publish details about users while some of the information revealed inside is private. In order to address privacy concerns, many social networks allow users to hide their private or sensitive information in their profiles from the public. In this paper, we focus on the problem of information revelation in online social networks by preserving the privacy of sensitive information in their data using machine learning and data mining algorithms. We show how an adversary can launch an inference or neighborhood attack to exploit an online social network using released data and structure of the network to predict the private information and attributes of users. For this purpose, we propose a new data mining based model that uses neighborhood information and attributes details of a user to infer private attributes of user profiles. The proposed model consists of two main parts: a clustering approach to ensure the k-anonymity and a classification algorithm to preserve the privacy against inference attacks. Finally we explore the effectiveness of some sanitization techniques that can be used to combat such inference attacks, and we show experimentally the success of different neighborhood re-identification strategies. Our experimental results reveal that using combination of data mining algorithm can notably help to preserve private and sensitive information in social network data.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Proposed Model for Social Network Anonymization
  3.1. Model structure
  3.2 Naive Anonymization
  3.3 Node Clustering
  3.4 Classification Task
  3.5 Sanitization Techniques
 4. Experimental Evaluation and Results
  4.1 Datasets
  4.2 Evaluation Metrics
  4.3. Experimental Results
 5. Conclusions
 References

키워드

저자

• Ali Zaghian [ Department of Mathematics and Cryptography, Malek Ashtar University of Technology, Isfahan, Iran ]
• Ayoub Bagheri [ Department of Electrical and Computer Engineering, university of Kashan, Isfahan, Iran ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Security and Its Applications

• 간기

  격월간

• pISSN

  1738-9976

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605