Back to Current Issues

Cross Stage Identification of Unknown Clients in Numerous Online Networking Systems

Ms. Tamreen Fatima, Dr. G.S.S Rao,

Affiliations
Nawab Shah Alam Khan College of Engineering and Technology, Hyd
:10.22362/ijcert/2017/v4/i10/xxxx [UNDER PROCESS]


Abstract
A previous couple of years have witnessed the emergence and evolution of a vivacious analysis stream on an oversized sort of online Social Media Network (SMN) platforms. Recognizing anonymous, nonetheless, same users among multiple SMNs continues to be AN intractable downside. Cross-platform exploration could facilitate solve several issues in social computing in each theory and applications. Since public profiles are often duplicated and impersonated merely by users with entirely different functions, most current user identification resolutions, which principally specialize in text mining of users’ public profiles, are fragile. Some studies have tried to match users supported the placement and temporal order of user content also as a genre. However, the locations are distributed within the majority of SMNs, and genre is tough to pick out from the short sentences of leading SMNs like Sina Microblog and Twitter. Moreover, since on-line SMNs are quite regular, existing user identification schemes supported network structure don't seem to be effective. The real-world friend cycle is extremely individual, and just about no 2 users share a congruent friend cycle. Therefore, it's additional correct to use a relationship structure to investigate cross-platform SMNs. Since same users tend to line up partial similar relationship structures in many SMNs, we tend to project the Friend Relationship-Based User Identification (FRUI) algorithmic rule. FRUI calculates an equal degree for all candidate User Matched Pairs (UMPs), and solely UMPs with high ranks are thought of as equal users. We tend to conjointly develop 2 propositions to enhance the potency of the algorithmic rule. Results of intensive experiments demonstrate that FRUI performs far better than current network structure-based algorithms.


Citation
Ms. Tamreen Fatima and Dr. G.S.S Rao (2017). Cross Stage Identification of Unknown Clients in Numerous Online Networking Systems . International Journal of Computer Engineering In Research Trends, 4(10), 400-406. Retrieved from http://ijcert.org/ems/ijcert_papers/V4I1005.pdf


Keywords : Cross-Platform, Social Media Network, Anonymous Identical Users, Friend Relationship, User Identification

References
[1]Wikipedia,"Twitter, "
http://en.wikipedia.org/wiki/Twitter. 2014.
[2] Xinhuanet, "Sina Microblog Achieves over 500
MillionUsers,"
http://news.xinhuanet.com/tech/2012-
02/29/c_122769084.htm. 2014.
[3] J. Liu, F. Zhang, X. Song, Y.I. Song, C.Y. Lin, and
H.W. Hon, "What's in a name?: an unsupervised
approach to link users across communities," Proc. of
the 6th ACM international conference on Web search
and data mining(WDM’13), pp. 495-504, 2013.
[4] O. Goga, D. Perito, H. Lei, R. Teixeira, and R.
Sommer, "Large-scale Correlation of Accounts across
Social Networks," Tech- nical report, 2013.
[5] K. Cortis, S. Scerri, I. Rivera, and S. Handschuh,
"An ontology-based technique for online profile
resolution," Social Informatics, Berlin: Springer, pp.
284-298, 2013.
[6] F. Abel, E. Herder, G.J. Houben, N. Henze, and D.
Krause, "Cross-system user modeling and
personalization on the social web," User Modeling and
User-Adapted Interaction, vol. 23, pp. 169-209, 2013.
[7] N. Korula and S. Lattanzi, "An efficient
reconciliation algorithm for social networks," arXiv
preprint arXiv:1307.1690, 2013.
[8] P. Jain, P. Kumaraguru, and A. Joshi, "@ i seek 'fb.
me': identify- ing users across multiple online social
networks," Proc. of the 22nd International Conference
on World Wide Web Companion, pp. 1259-1268,
2013.
[9] X. Kong, J. Zhang, and P.S. Yu, "inferring anchor
links across multiple heterogeneous social networks,"
Proc. of the 22nd ACM International Conf. on
Information and Knowledge Management (CIKM’13),
pp. 179-188, 2013.
[10] O. Goga, H. Lei, S.H.K. Parthasarathi, G.
Friedland, R. Sommer, and R. Teixeira, "Exploiting
innocuous activity for correlating us- ers across sites,"
Proc. 22nd international conference on World Wide
Web (WWW’13),pp. 447-458, 2013.
[11] R. Zafarani and H. Liu, "Connecting users across
social media sites: a behavioral-modeling approach, "
Proc. of the 19th ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining
(KDD’13), pp.41-49, 2013.
[12] S. Bartunov, A. Korshunov, S. Park, W. Ryu, and
H. Lee, "Joint link-attribute user identity resolution in
online social net- works," The 6th SNA-KDD
Workshop ’12, 2012.
[13] P. Jain and P. Kumaraguru, "Finding Nemo:
searching and re-solving identities of users acrossonline social networks," arXiv preprint
arXiv:1212.6147, 2012.
[14] M. Almishari and G. Tsudik, "Exploring
linkability of user re- views," Computer Security–
ESORICS 2012 (ESORICS’12), pp. 307- 324, 2012.
[15] D. Perito, C. Castelluccia, M.A. Kaafar, and P.
Manils, "How unique and traceable are usernames?,"
Privacy Enhancing Technol- ogies (PETS’11), pp. 1-
17, 2011.
[16] A. Acquisti, R. Gross and F. Stutzman, "Privacy
in the age of aug- mented reality," Proc. National
Academy of Sciences, 2011.
[17] T. Iofciu, P. Fankhauser, F. Abel, and K. Bischoff,
"Identifying users across social tagging systems,” Proc.
of the 5th International AAAI Conference on Weblogs
and Social Media, pp. 522-525, 2011.
[18] B. Zhou and J. Pei, "The k-anonymity and ldiversity
approaches for privacy preservation in social
networks against neighborhood attacks," Knowl. Inf.
Syst, vol. 28, no. 1,pp. 47-77, 2011.
[19] E. Raad, R. Chbeir, and A. Dipanda, "User profile
matching in social networks," Proc. Of the 13th
International Conference on Network-Based
Information Systems (NBiS’10), pp.297-304, 2010.
[20] P. Erd?s and A. Rényi, "On random graphs I,"
Publ. Math. De- brecen, vol. 6, pp. 290-297, 2010.
[21] R. Zafarani and H. Liu, "Connecting
corresponding identities across communities," Proc. of
the 3rd International ICWSM Con- ference, pp. 354-
357, 2009.
[22] M. Motoyama and G. Varghese, "I seek you:
searching and matching individuals in social
networks," Proc. of the 11th inter- national workshop
on Web Information and Data Management
(WIDM’09), pp. 67-75, 2009.
[23] J. Vosecky, D. Hong, and V.Y. Shen, "User
identification across multiple social networks," Proc.
Of the 1st International Confer- ence on Networked
Digital Technologies, pp.360-365, 2009.
[24] A. Narayanan and V. Shmatikov, "Deanonymizing
social net- works," Proc. Of the 30th
IEEE Symposium on Security and Privacy (SSP’09),
pp. 173-187, 2009.
[25] B. Zhou and J. Pei, "Preserving privacy in social
networks against neighborhood attacks," Proc. Of the
24th IEEE International Conference on Data
Engineering (ICDE’08), pp. 506–515, 2008.
[26] L. Backstrom, C. Dwork, and J. Kleinberg,
"Wherefore art thou r3579x?: anonymized social
networks, hidden patterns, and structural
steganography," Proc. of the 16th international conference
on World Wide Web(WWW’07), pp. 181-190,
2007.
[27] R. Zheng, J. Li, H. Chen, and Z. Huang, "A
framework for au- thorship identification of online
messages: writing? style fea? tures and classification
techniques," J. of the American Society for
Information Science and Technology, vol. 57, no. 3,
pp. 378-393, 2006.
[28] O. De Vel, A. Anderson, M. Corney, and G.
Mohay, "Mining e-mail content for author
identification forensics,” ACM Sigmod Record, vol.
30, no. 4, pp. 55-64, 2001.
[29] A. L. Barabasi and R. Albert, "Emergence of
scaling in random networks," Science, vol. 286, no.
5439, pp. 509-512, 1999.
[30] D.J. Watts, and S.H. Strogatz, "Collective
dynamics of ‘small-world’ networks," Nature, vol.393,
no.6684, pp. 440-442, 1998.
[31].K.Arun ,A.SrinageshandM.Ramesh,”Twitter
Sentiment Analysis on Demonetization tweets in India
Using R language.”International Journal of Computer
Engineering in Research Trends., vol.4, no.6, pp. 252-
258, 2017.
[32]TekurVijetha, M.SriLakshmi
andDr.S.PremKumar,”Survey on Collaborative
Filtering and content-Based
Recommending.”International Journal of Computer
Engineering in Research Trends., vol.2, no.9, pp. 594-
599, 2015.
[33]N.Satish Kumar, SujanBabuVadde,”Typicality
Based Content-BoostedCollaborative Filtering
RecommendationFramework.”International Journal of
Computer Engineering in Research Trends., vol.2,
no.11, pp. 809-813, 2015.
[34]D.Ramanjaneyulu,U.Usha Rani,”In Service-
Oriented MSN ProvidingTrustworthy Service
Evaluation.”International Journal of Computer
Engineering in Research Trends., vol.2, no.12, pp.
1192-1197, 2015.
[35]B.Kundan,N.Poorna Chandra Rao and
DrS.PremKumar,”Investigation on Privacy and Secure
content of location based Queries.”International
Journal of Computer Engineering in Research Trends.,
vol.2, no.9, pp. 543-546, 2015.


DOI Link : Not Yet Assigned

Download :
  V4I1005.pdf


Refbacks : Currently There are no refbacks

Quick Links


DOI:10.22362/ijcert


Science Central

Score: 13.30



Submit your paper to editorijcert@gmail.com

>