A SURVEY ON PRIVACY PRESERVING TECHNIQUES FOR SOCIAL NETWORK DATA
DOI:
https://doi.org/10.22159/ajpcr.2017.v10s1.19587Keywords:
Privacy models, K-anonymity, L-diversity, Social networks, Graph perturbation, RandomizationAbstract
In this era of 20th century, online social network like Facebook, twitter, etc. plays a very important role in everyone's life. Social network data, regarding any individual organization can be published online at any time, in which there is a risk of information leakage of anyone's personal data. So preserving the privacy of individual organizations and companies are needed before data is published online. Therefore the research was carried out in this area for many years and it is still going on. There have been various existing techniques that provide the solutions for preserving privacy to tabular data called as relational data and also social network data represented in graphs. Different techniques exists for tabular data but you can't apply directly to the structured complex graph  data,which consists of vertices represented as individuals and edges representing some kind of connection or relationship between the nodes. Various techniques like K-anonymity, L-diversity, and T-closeness exist to provide privacy to nodes and techniques like edge perturbation, edge randomization are there to provide privacy to edges in social graphs. Development of new techniques by Integration to exiting techniques like K-anonymity ,edge perturbation, edge randomization, L-Diversity are still going on to provide more privacy to relational data and social network data are ongoingin the best possible manner.
Â
Downloads
References
There exist various techniques for privacy preserving and in that utility (usefulness) of anonymized data is a serious impact. Methodologies should be developed in a way that quantitatively measure utility of data. Develop a method that can evaluate the various techniques in terms of tradeoff among privacy and utility.
Despite of all existing techniques like k-anonymity ,L-diversity , integrated approach of K-anonymity and L-Diversity has been developed for privacy preserving in social network but the problems of privacy is still coming because there is a loss of more information.
Different anonymization techniques has been developed so far but all are performing operations on static datasets or single time released dataset but social network sites are generating dynamic data continuously so new techniques should be developed to operate on dynamic datasets that provides privacy instantaneously.
All the privacy preserving approaches tries to evaluate the privacy in social network taking into consideration of small datasets or synthetic datasets. There is need to perform new experiments on existing techniques considering large datasets.
Many techniques exist like K-anonymity, L-Diversity that protects only nodes and only few techniques exits that provides privacy to edges like edge perturbation, edgerandomization. There is a need to develop more techniques that provides privacy by protecting sensitive edges between nodes.
Not even single technique exists that prevents from all types of attacks like homogeneity attacks, background knowledge attacks, sensitive edge attacks. There is need to develop a technique that gives preserves privacy from all types of attacks.
Conclusion
Various techniques have been developed till now that provides privacy in tabular micro-data are K-anonymity, L-diversity, t-closeness and integrated approach of these techniques but all techniques have some drawbacks that lead to information loss and no technique exist that provides privacy in all aspects like protecting nodes,protecting edges as well as both. So, there is big scope of improving the existing techniques foe social data that gives minimum information loss and that gives better utility of released data. Also there is scope in improving the edge privacy techniques like edge randomization and edge perturbation so that network of nodes and edges will be safe after releasing information of individual.
References
Elizabeth Phillips , Jason Nurse , Michael Goldsmith and Sadie Creese,â€Applying social network analysis to securityâ€,ICCSSS,2015
Lzang ,S Xu, T Bylander, J Ruan, R Krishnan,â€Privacy Preserving in social graphsâ€,Citeseer ,2012
Bin Zhou ,Jian Pei ,W0-Shun Luk : A Brief Survey On Anonymization techniques for Privacy Preserving Publishing of Social Network Dataâ€,2008.
A. F. Westin, Privacy and freedom vol. 97: London, 1967.
Kun Liu, Kamalika Das, Tyrone Grandison, HillolKargupta, "Privacyâ€preserving data analysis on graphs and social networks," In : Next Generation of Data Mining, pp. 419â€437, 2008.
Benjamin C. M. Fung, Ke Wang, Rui Chen, Philip S. Yu, "Privacyâ€preserving data publishing: A survey of recent developments," In: ACM Computing Surveys (CSUR), Vol. 42, pp 1â€53, 2010.
P. Samarati, L. Sweeney, "Protecting privacy when disclosing information: kâ€anonymity and its enforcement through generalization and suppression," In: IEEE Transactions on Knowledge and Data Engineering, 2001.
AshwinMachanavajjhala, Daniel Kifer, Johannes Gehrke, "lâ€diversity: Privacy beyond kâ€anonymity," In: ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 1, 2007.
Ninghui Li, Tiancheng Li, Suresh Venkatasubramanian, "tâ€closeness: Privacy beyond kâ€anonymity and lâ€diversity," In Proc. of 23rd International Conference on Data Engineering ICDE 2007, IEEE, Istanbul, pp 106â€115, 2007.
Xiaoxun Sun, Hua Wang, Jiuyong Li, Traian Marius Truta, "Enhanced Pâ€sensitive Kâ€anonymity models for privacy preserving data publishing," In: Transactions on Data Privacy, vol. 1, pp 53â€66, 2008.
X. Xiao, Y. Tao, "Mâ€invariance: towards privacy preserving reâ€publication of dynamic datasets," In Proc. of International conference on Management of data SIGMOD '07, ACM, New York, NY, USA, pp 689-700, 2007.
Bee-Chung Chen, Kristen LeFevre, Raghu Ramakrishnan, "Privacy skyline: Privacy with multidimensional adversarial knowledge," In Proc. of 33rd International conference on Very large data bases VLDB '07, pp 770-781, 2007.
L. Sweeney, "kâ€anonymity: A model for protecting privacy," In: International Journal of Uncertainty Fuzziness and Knowledge Based Systems, Vol. 10, pp. 557â€570, 2002.
Mehmet ErcanNergiz, Christopher Clifton,AhmetErhanNergiz,†Multirelational K-anonymityâ€, IEEE Transactions on Knowledge and Data Engineering, vol.21,Issue No. 08,2009
AshwinMachanavajjhala, Daniel Kifer, Johannes Gehrke, "lâ€diversity: Privacy beyond kâ€anonymity," In: ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 1, 2007.
Ninghui Li, Tiancheng Li, Suresh Venkatasubramanian, "tâ€closeness: Privacy beyond kâ€anonymity and lâ€diversity," In Proc. of 23rd International Conference on Data Engineering ICDE 2007, IEEE, Istanbul, pp 106â€115, 2007.
B.K.Tripathy ,AniribanMitra,An Algorithm to achieve k-anonymity and l-diversity anonymisation in Social Networks†,Fourth International Conference on Computational Aspects of Social Networks (CASoN), 978-1-4673-4794-5/12,IEEE,2012.
Bin Zhou ,Jian Pei ,W0-Shun Luk : A Brief Survey On Anonymization techniques for Privacy Preserving Publishing of Social Network Data,2008.
M. Hay, G. Miklau, D. Jensen, and D. Towsley. Resisting structural identification in anonymized social net-works. In Proceedings of the 34th International Conference on Very Large Databases (VLDB'08). ACM, 2008.
E. Zheleva and L. Getoor. Preserving the privacy of sensitive relationships in graph data. In Proceedings ofthe 1st ACM SIGKDD Workshop on Privacy, Security, and Trust in KDD (PinKDD'07), 2007.
G. Cormode, D. Srivastava, T. Yu, and Q. Zhang. Anonymizing bipartite graph data using safe groupings.In Proceedings of the 34th International Conference on Very Large Databases (VLDB'08). ACM, 2008.
N.Punitha and R.Amsaveni : Methods and Techniques to protect the privacy information in Privacy Preservation Data Mining, International Journal of Computer Technology Applications, Vol 2960,2091-2097.
K. Liu and E. Terzi. Towards identity anonymization on graphs. In Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data (SIG- MOD'08), pages 93-106, New York, NY, USA, 2008.ACM Press.
G.K.Panda, A. Mitra, Ajay Prasad, Arjun Singh, Deepak Gour, Applying l-Diversity in anonymizing collaborative social network†In: International Journal of Computer Science and Information Security, Vol 8, Issue 2, pp 324 - 329, 2010.
SanazKavianpour, Zuraini Ismail, and AmirhosseinMohtaseb, Preserving Identity Of Users In Social Network Sites By Integrating Anonymization And Diversification Algorithmsâ€, In: International Journal of Digital Information and Wireless Communications (IJDIWC), Hongkong, Vol. 1, Issue 1, pp 32-40, 2011.
AmirrezaMasoumzadeh, James Joshi- Preserving Structural Properties in Edge-Perturbing Anonymization Techniques for Social Networks, IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING, VOL. 9, NO. 6, NOVEMBER/DECEMBER 2012.
Mayank Singh Shishodia ,Sumeet Jain and B.K.Tripathy: GASNA-Greedy Algorithm for Social Network Anonymization, IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining/2013.
AnirbanChakraborty ,Annappa B- A Perturbation Based approach for Privacy Preserving Publication of Social Network Graphs,sps2014.
Lian Liu-Privacy preserving data mining for numerical matrices ,Social networks and bigdata,University of Kentucy,2015
SaikatGuha, Kevin Tang, Paul Francis, NOYB: Privacy in Online Social Networksâ€, in Proc. of first workshop on Online social networks WOSN’08, ACM New York, NY, USA, pp 49-54, 2008.
Gary Blosser, Justin Zhan, Privacy Preserving Collaborative Social Networkâ€, In Proc. of International Conference on Information Security and Assurance ISA 2008, Busan, pp, 543 - 548, 2008.
AlinaCampan, Traian Marius Truta, Nicholas Cooper, P-Sensitive K-Anonymity with Generalization Constraintsâ€, In: Transactions on Data Privacy archive, Vol. 3 Issue 2, pp 65-89, 2008.
Roy Ford, Traian Marius Truta, and AlinaCampan, P-Sensitive K-Anonymity for Social Networksâ€.
A. Narayanan, V. Shmatikov, "Deâ€anonymizing social networksâ€, In Proc of 30th IEEE Symposium on Security and Privacy, Berkeley, CA, pp 173â€187, 2009.
Z. Lijie and Z. Weining, "Edge Anonymity in Social Network Graphs," in Proc. of International Conference on Computational Science and Engineering CSE '09, pp 1â€8, 2009.
Philip W. L. Fong, Mohd Anwar, and Zhen Zhao, A Privacy Preservation Model for Facebook-Style Social Network Systemsâ€, In: Computer Security - ESORICS 2009, Lecture Notes in Computer Science, Vol. 5789, 2009, pp 303-320, 2009. 37.
X. Tang and C.C. Yang, "Generalizing Terrorist Social Networks with K-Nearest Neighbor and Edge Betweenness for Social Network Integration and Privacy Preservation," In Proc. of IEEE International Conference on Intelligence and Security Informatics, 2010.
Xuan Ding, Lan Zhang, Zhiguo Wan, and Ming Gu, A Brief Survey on De-anonymization Attacks in Online Social Networksâ€, In Proc. of International Conference on Computational Aspects of Social Networks, Taiyuan, pp 611 - 615, 2010.
Xintao Wu, Xiaowei Ying, Kun Liu, ,Lei Chen, A Survey of Privacy-Preservation of Graphs and Social Networksâ€, In : Managing and Mining Graph Data, Advances in Database Systems, Vol. 40, pp 421-453, 2010.
Elena Zheleva, LiseGetoor, Privacy in Social Networks: A Surveyâ€, In: Social Network Data Analytics, Springer US, pp 277-306, 2011.
AmirrezaMasoumzadeh, James Joshi, Preserving Structural Properties in Edge-Perturbing Anonymization Techniques for Social Networksâ€, In: IEEE Transactions On Dependable And Secure Computing, Vol. 9, No. 6, pp 877-889, 2012.
Raymond Heatherly, Murat Kantarcioglu, and BhavaniThuraisingham, Preventing Private Information Inference Attacks on Social Networksâ€, In: IEEE Transactions On Knowledge And Data Engineering, Vol. 25, No. 8, pp 1849-1862, 2013.
Yuan Cheng, Ravi Sandhu, Preserving User Privacy from Third-party Applications in Online Social Networks, In Proc. of 22nd international conference on World Wide Web companion, Geneva, Switzerland, pp 723-728, 2013.
V.Gnanasekar, S.Jayanthi, Privacy Preservation of Social Network Data against Structural Attack using K-Auto restructureâ€, International Journal of Computer Science and Information Technologies, Vol. 5 (2) , 2014, 1375-1381
Chongjing Sun *, Philip S Yu∗∗, Xiangtan Kong∗∗, Yan Fu, â€Privacy Preserving Social Network Publication Against Mutual Friend Attacksâ€, TRANSACTIONS ON DATA PRIVACY 7 (2014) 71–97.
Suma Reddy, Shilpa G V, Privacy Preserving Publishing of Social Network Data Privacy and Big Data Miningâ€, International Journal of Emerging Technology and Advanced Engineering Website: ISSN 2250-2459 (Online), Volume 5, Special Issue 2, May 2015.
V.Vijeya Kaveri, Dr.V.Maheswari, Cluster Based Anonymization for Privacy Preserving in Social Network Data Communityâ€, Journal of Theoretical and Applied Information Technology 20th March 2015. Vol.73 No.2
N. Sridhar, Y. Srinivas, Preview and Maximizing the Data Utility in Privacy Preserving Social Networkâ€, International Journal of Computer Applications (0975 – 8887) Volume 130 – No.3, November 2015.
Published
How to Cite
Issue
Section
The publication is licensed under CC By and is open access. Copyright is with author and allowed to retain publishing rights without restrictions.
