NDLI: Preserving weighted social networks privacy using vectors similarity

Content Provider	IEEE Xplore Digital Library
Author	Lan, Lihui
Copyright Year	2015
Description	Author affiliation: School of Computer Science and Telecommunication Engineering, JiangSu University, ZhenJiang, JiangSu, China, School of Information Engineering, Shenyang University, Shenyang, China (Lan, Lihui)
Abstract	Aiming at weighted social networks, a random perturbation method based on vectors similarity is proposed. It can protect structures and edge weights of weighted social networks in multiple release scenarios. First, it partitions weighted social networks into t sub-graphs by the segmentation method based on vertex cluster using edge space of graph theory, describes these sub-graphs by vectors, and constructs vector set models of weighted social networks. Then, it adopts weighted Euclidean distance as the metrics of vectors similarity to construct the released candidate sets of t sub-graphs according to the threshold designated by publishers. Finally, it randomly selects vectors from the candidate sets to construct the released vector set, and builds the published weighted social networks based on the released vector set. The proposed method can resist multiple vertex recognition attacks, force the attackers to re-identify in a large result set that the existential probabilities of the vectors are same, and increase the uncertainty of recognition. The experimental results on the actual datasets demonstrate that the proposed method can preserve the security of individuals' privacy, meanwhile it can protect some structure characteristics for social networks analysis and improve the released data utility.
Starting Page	789
Ending Page	794
File Size	342244
Page Count	6
File Format	PDF
e-ISBN	9781509000227
DOI	10.1109/BMEI.2015.7401610
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Privacy Social network services Euclidean distance vectors similarity privacy preserving weighted Euclidean distance Silicon Graph theory weighted social networks vector set model Erbium
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

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