NDLI: A New Approach to Manage Security against Neighborhood Attacks in Social Networks

Content Provider	IEEE Xplore Digital Library
Author	Tripathy, B.K. Panda, G.K.
Copyright Year	2010
Abstract	Now a days, more and more of social network data are being published in one way or other. So, preserving privacy in publishing social network data has become an important concern. With some local knowledge about individuals in a social network, an adversary may attack the privacy of some victims easily. Most of the work done so far towards privacy preservation can deal with relational data only. However, Bin Zhou and Jian Pei [11] proposed a scheme for anonymization of social networks, which is an initiative in this direction and provides a partial solution to this problem. In fact, their algorithm cannot handle the situations in which an adversary has knowledge about vertices in the second or higher hops of a vertex, in addition to its immediate neighbors. In this paper, we propose a modification to their algorithm for the network anonymization which can handle such situations. In doing so, we use an algorithm for graph isomorphism based on adjacency matrix instead of their approach using DFS technique [11]. More importantly, the time complexity of our algorithm is less than that of Zhou and Pei.
Starting Page	264
Ending Page	269
File Size	757807
Page Count	6
File Format	PDF
ISBN	9781424477876
DOI	10.1109/ASONAM.2010.69
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-09
Publisher Place	Denmark
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Data privacy Privacy Anonymization Label hierarchy and Adjacency matrix Social network services Graph isomorphism Neighborhood attack Educational institutions Loss measurement Security
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Personalized anonymity in social networks data publication

Anonymizing Graphs Against Weight-Based Attacks

Preserving Privacy in Social Networks Against Neighborhood Attacks

Resisting label-neighborhood attacks in outsourced social networks

Preserving privacy in social networks against neighborhood attacks.

StarClique: guaranteeing user privacy in social networks against intersection attacks

K-isomorphism: privacy preserving network publication against structural attacks

Preserving privacy in social networks against subgraph attacks

Graph Anonymization Using Machine Learning

A New Approach to Manage Security against Neighborhood Attacks in Social Networks

Similar Documents

Personalized anonymity in social networks data publication

Anonymizing Graphs Against Weight-Based Attacks

Preserving Privacy in Social Networks Against Neighborhood Attacks

Resisting label-neighborhood attacks in outsourced social networks

Preserving privacy in social networks against neighborhood attacks.

StarClique: guaranteeing user privacy in social networks against intersection attacks

K-isomorphism: privacy preserving network publication against structural attacks

Preserving privacy in social networks against subgraph attacks

Graph Anonymization Using Machine Learning

A New Approach to Manage Security against Neighborhood Attacks in Social Networks