Popular article Social network ranking algorithm anonymization

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Xing Xie at Microsoft Research
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Xing Xie at Microsoft Research

Date Jan 5, 2018

Nowadays, partly driven by many Web 2. 0 applications, more and more social network data has been made publicly available and analyzed in one way or another. . A system and method for detecting fake accounts in OSNs is proposed to aid the OSN provider 20 against fake users, wherein a social graph G of the OSN, with n nodes, a non-Sybil region G H and a Sybil region G S , is obtained and the following steps are performed: a trust value T (i) (v) is computed through i power iterations on each node …

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Instability vs anonymization in E Pluribus Hugo
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Instability vs anonymization in E Pluribus Hugo

Date Jan 5, 2018

De-anonymization of Mobility Trajectories: Dissecting the Gaps between Theory and Practice Parameters chosen by empirical values or estimated by EM algorithm n2) Modelling Users’ Mobility Pattern: Markov Model , and F. Zambonelli, “Re-identification and information fusion between anonymized cdr and social network data,” Journal. A Survey on Attack Prevention and Handling - Download as PDF File (. pdf), Text File (. txt) or read online.

A Survey on Attack Prevention and Handling - Computer
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A Survey on Attack Prevention and Handling - Computer

Date Jan 9, 2018

Shouling Ji, Weiqing Li, Mudhakar Srivatsa, Jing Selena He, and Raheem Beyah, General Graph Data De-anonymization: From Mobility Traces to Social Networks, ACM Transactions on Information and System Security (TISSEC), 2016. . De#Anonymization De-anonymizing Social Networks and Inferring Private Attributes Using Knowledge Graphs 6 Path,Ranking Algorithm Private Attributes Using Knowledge Graphs 30 Metrics: hit@k, MRR (Mean …

Analysis of Grasshopper, a Novel Social Network De
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Analysis of Grasshopper, a Novel Social Network De

Date Jan 3, 2018

How can we find patterns, communities, outliers, in a social network? Which are the most central nodes in a network? and anonymization. Then we provide a list of powerful tools for graph analysis, and specifically spectral methods (Singular Value Decomposition tensors, and case studies like the famous “pageRank” algorithm and the. In this thesis, we focus on social network graph anonymization methods that in the network. We present a ranking algorithm that is able to e ciently identify the top n weakest nodes in the network. Our anonymization algorithm then anonymizes

Philip Yu - UIC Distinguished Professor Wexler Chair
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Philip Yu - UIC Distinguished Professor Wexler Chair

Date Jan 1, 2018

De-anonymizing Social Graphs via Node Similarity Hao Fu University of Science and Technology of China fuch@mail. ustc. edu. cn social network 1. INTRODUCTION In social networking sites, users and their social ties can be described as a social graph. In order to satisfy the need anonymization algorithm based on the measurement. As it. A system has been implemented which combines a synthetic graph social network data generator with a strict anonymization method which uses an efficient local sub-graph matcher to mitigate the information loss (anonymization cost) and optimize the data utility for local neighborhood social network structures.

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Webdam Project Anonymization
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Webdam Project Anonymization

Date Jan 2, 2018

View Philip Yu’s profile on LinkedIn, the world's largest professional community. ranking Top-10 globally in computer science and electronics (as of Sept. 2018). social network analysis

Volume 4, Issue 5, May 2014 ISSN: 2277 128X
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Volume 4, Issue 5, May 2014 ISSN: 2277 128X

Date Jan 3, 2018

o map users. For each network, we build a social hyper- Notation and Problem Definition Figure 1: An example for mapping users across networks.

Class-based graph anonymization for social network data
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Class-based graph anonymization for social network data

Date Jan 9, 2018

Blind De-anonymization A˛acks using Social Networks Wei-Han Lee Princeton University weihanl@princeton. edu de-anonymize a set of location traces based on a social network. Blind De-anonymization Attacks using Social Networks Author: Wei-Han Lee, Changchang Liu, Shouling Ji, Prateek Mittal, and Ruby B. Lee

Anonymizing social networks: A generalization approach
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Anonymizing social networks: A generalization approach

Date Jan 17, 2018

Since social network data is richer in details about the users and their interactions, loss of details due to anonymization limits the possibility for analysis. We present a new set of techniques for anonymizing social network data based on grouping the entities into classes, and masking the mapping between entities and the nodes that represent