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  1. Proceedings of the 7th International Workshop on Hot Topics in Planet-scale mObile computing and online Social neTworking (HOTPOST '15)
  2. Privacy Preserving Social Tie Discovery Based on Cloaked Human Trajectories
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Inter Action Time in Virtual and Physical Spaces: A Comparative Study
Privacy Preserving Decentralized Identity Validation for Geo-social Networks over MANET
Exploiting User Context and Network Information for Mobile Application Usage Prediction
An Empirical Study of Human Altruistic Behaviors in Opportunistic Networks
Privacy Preserving Social Tie Discovery Based on Cloaked Human Trajectories
Video Highlight Shot Extraction with Time-Sync Comment
Evaluating the Influence of Social Selfishness on Cooperative D2D Communications

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Privacy Preserving Social Tie Discovery Based on Cloaked Human Trajectories

Content Provider ACM Digital Library
Author Tian, Ye Kou, Qinli Song, Zheng Ngai, Edith Wang, Wendong
Abstract Discovering social connections of people has become a flourishing research topic considering the rich social information inferable from human trajectories. Existing social tie detection methods often require exact locations of users, which cause serious privacy concerns. Although cloaking is a common technique for location anonymization, it has rarely been applied in social tie detection due to the potential loss of significant location information. In this paper, we propose a semantic tree model for social tie detection, which supports different levels of privacy preserving and allows better understanding of location content of the cloaking regions. We propose a novel algorithm that can infer the social ties between users using only their cloaked trajectories without exposing their exact locations. We model the obscured regions generated by the cloaking algorithms in a semantic region tree and infer the similarity between two users based on their temporal and spatial relations in the tree. We evaluate our proposed approach using real trajectory dataset and show that our algorithm can identify social ties successfully with 15% higher accuracy compared with existing approach.
Starting Page 13
Ending Page 18
Page Count 6
File Format PDF
ISBN 9781450335171
DOI 10.1145/2757513.2757522
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2015-06-22
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Location privacy Social tie discovery Human trajectory
Content Type Text
Resource Type Article
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