On Your Social Network De-anonymizablity: Quantification and Large Scale Evaluation with Seed Knowledge

نویسندگان

  • Shouling Ji
  • Weiqing Li
  • Neil Zhenqiang Gong
  • Prateek Mittal
  • Raheem A. Beyah
چکیده

In this paper, we conduct the first comprehensive quantification on the perfect de-anonymizability and partial deanonymizability of real world social networks with seed information in general scenarios, where a social network can follow an arbitrary distribution model. This quantification provides the theoretical foundation for existing structure based de-anonymization attacks (e.g., [1][2][3]) and closes the gap between de-anonymization practice and theory. Besides that, our quantification can serve as a testing-stone for the effectiveness of anonymization techniques, i.e., researchers can employ our quantified structural conditions to evaluate the potential deanonymizability of the anonymized social networks. Based on our quantification, we conduct a large scale evaluation on the de-anonymizability of 24 various real world social networks by quantitatively showing: 1) the conditions for perfect and (1− ε) de-anonymization of a social network, where ε specifies the tolerated de-anonymization error, and 2) the number of users of a social network that can be successfully de-anonymized. Furthermore, we show that, both theoretically and experimentally, the overall structural information based de-anonymization attack is much more powerful than the seed knowledge-only based deanonymization attack, and even without any seed information, a social network can be perfectly or partially de-anonymized. Finally, we discuss the implications of this work. Our findings are expected to shed light on the future research in the structural data anonymization and de-anonymization area, and to help data owners evaluate their structural data vulnerability before data sharing and publishing.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

[Article] Social Network De-anonymization Under Scale-free User Relations

We tackle the problem of user de-anonymization in social networks characterized by scale-free relationships between users. The network is modeled as a graph capturing the impact of power-law node degree distribution, which is a fundamental and quite common feature of social networks. Using this model, we present a de-anonymization algorithm that exploits an initial set of users, called seeds, t...

متن کامل

On The Role Of Centrality In Information Diffusion In Social Networks

Towards understanding how people use social media to interact with each other, it becomes important to investigate the influence mechanism that emerges in online social networks. Due to it, information in social networks often diffuses “virally”. In this paper, we recognize the relation between the influence of the seed members that trigger information diffusion and the attitude of the rest mem...

متن کامل

DISCRETE AND CONTINUOUS SIZING OPTIMIZATION OF LARGE-SCALE TRUSS STRUCTURES USING DE-MEDT ALGORITHM

Design optimization of structures with discrete and continuous search spaces is a complex optimization problem with lots of local optima. Metaheuristic optimization algorithms, due to not requiring gradient information of the objective function, are efficient tools for solving these problems at a reasonable computational time. In this paper, the Doppler Effect-Mean Euclidian Distance Threshold ...

متن کامل

Investigation on Biochemically Processed Castor Seed Meal in Nutrition and Physiology of Japanese Quails

Native de-oiled and treated castor seed meal was subjected to proximate analysis and quantification of anti-nutrients (phytochemicals). Seed cake was treated by biochemical technique of solid state fermentation with Aspergillus niger and addition of calcium oxide (CaO) to give treated castor seed meal (TCSM). One hundred and twenty Japanese quails (Coturnix coturnix japonica) were fed four (4) ...

متن کامل

Growing a Graph Matching from a Handful of Seeds

In many graph–mining problems, two networks from different domains have to be matched. In the absence of reliable node attributes, graph matching has to rely on only the link structures of the two networks, which amounts to a generalization of the classic graph isomorphism problem. Graph matching has applications in social–network reconciliation and de-anonymization, protein–network alignment i...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2015