Privacy Protection on Social Network Data Using Anonymization Methodology

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Privacy is amongst the major concerns when publishing or sharing social network data for social science research and also business analysis. Recently, researchers have developed privacy models just like k-anonymity in order to avoid node reidentification through structure information. However, even though these privacy models are enforced, an attacker can always have the capacity to infer one’s information that is personal if the list of nodes largely shares the identical sensitive labels (i.e., attributes). To put it differently, the label-node relationship just isn't thoroughly protected by pure structure anonymization methods. We present privacy protection algorithms which facilitate graph data to become published within a form in a way that an adversary who possesses information regarding a node's neighborhood cannot safely infer its identity as well as sensitive labels. To the present aim, the algorithms transform an original graph in to a graph during which nodes are sufficiently indistinguishable. The algorithms are created to accomplish that while losing only small amount information and even though preserving the maximum amount of Utility as possible.

Published In : IJCAT Journal Volume 1, Issue 7

Date of Publication : 31 August 2014

Pages : 363 - 366

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Publication Link : Privacy Protection on Social Network Data Using Anonymization Methodology

D. Shalini Reddy : Post-Graduate Student, Department of Computer Science and Engineering, SIT, PUTTUR, India

K. Narayana : Head & Associate Professor, Department of Computer Science and Engineering, SIT, PUTTUR, India

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In this particular paper, k-degree-l-diversity model has implemented for privacy preserving social network data publishing. Implementation of both distinct l-diversity and recursive (c, l)-diversity also happened. So that you can attain the dependence on k-degree-l-diversity, a noise node adding algorithm to make A whole new graph through the original graph while using the constraint of introducing fewer distortions for the original graph. Rigorous analysis of the theoretical bounds within the variety of noise nodes added and their impacts by using an important graph property. Extensive experimental results demonstrate how the noise node adding algorithms can performs a much better result compared to the previous work of edge editing method. In a very distributed environment, data publication satisfy certain privacy requirements, a hacker can still collapse privacy by connecting the information by different users. Similar Protocols needs to be developed to conserve the data publishers to assure the Privacy preservation.

[1] C wang, Sherman S. M. Chow, Q. Wang, K Ren and W.Lou, “Privacy-Preserving Public Auditing for Secure Cloud Storage”,IEEE Trasaction on Computers I, vol. 62, no. 2, pp.362-375 , February 2013.

[2] C. Wang, Q. Wang, K. Ren, and W. Lou, “Privacy Preserving Public auditing for storage security in cloud computing,” in Proc.of IEEE INFOCOM’10, March 2010.

[3] Wang Shao-hu, Chen Dan-we, Wang Zhi-weiP, Chang Su-qin, “Public auditing for ensuring cloud data storage security with zero knowledge Privacy” College of Computer, Nanjing University of Posts and Telecommunications, China, 2009

[4] KunalSuthar, Parmalik Kumar, Hitesh Gupta, “SMDS: secure Model for Cloud Data Storage”, International Journal of Computer applications, vol56, No.3, October 2012

[5] AbhishekMohta, Lalit Kumar Awasti, “Cloud Data Security while using Third Party Auditor”, International Journal of Scientific & Engineering Research, Volume 3,Issue 6, ISSN 2229-8 June 2012.

[6] Q. Wang, C. Wang,K.Ren, W. Lou and Jin Li “Enabling Public Audatability and Data Dynamics for Storage Security in Cloud Computing”, IEEE Transaction on Parallel and Distributed System, vol. 22, no. 5, pp. 847 – 859,2011.

[7] D. Shrinivas, “Privacy-Preserving Public Auditing in Cloud Storage security”, International Journal of computer science nad Information Technologies, vol 2, no. 6, pp.2691-2693, ISSN: 0975-9646, 2011

[8] K Govinda, V. Gurunathprasad and H. sathishkumar, “ Third Party Auditing for Secure Data Storage in Cloud Through Digital Signature Using RSA”, International Journal of Advanced science and Technical Research, vol 4,no. 2, ISSN: 2249-9954,4 August 2012

[9] S. Marium, Q. Nazir, A. Ahmed, S. Ahthasham and Aamir M. Mirza, “Implementation of EAP with RSA for Enhancing The Security of Cloud Computig”, International Journal of Basic and Applied Science, vol 1, no. 3, pp. 177- 183, 2012

[10] XU Chun-xiang, HE Xiao-hu, Daniel Abraha, “Cryptanalysis of Auditing protocol proposed by Wang et al. for data storage security in cloud computing”, http://eprint.iacr.org/2012/115.pdf, and cryptologyeprintarchieve: Listing for 2012.

[11] B. Dhiyanesh “A Novel Third Party Auditability and Dynamic Based Security in Cloud Computing” ,International Journal of Advanced Research in Technology, vol. 1,no. 1, pp. 29-33, ISSN: 6602 3127, 2011

[12] C. Wang, Q. Wang and K. Ren, “Ensuring Data Storage security in Cloud Computing”, IEEE Conference Publication, 17th International Workshop on Quality of Service (IWQoS), 2009

[13] Balkrishnan. S, Saranya. G, Shobana. S and Karthikeyan. S, “Introducing Effective Third Party Auditing (TPA) for Data Storage Security in Cloud”, International Journal of computer science and Technology, vol. 2, no. 2, ISSN 2229-4333 (Print) | ISSN: 0976-8491(Online), June 2012.

[14] Sushmita Ruj, Milos Stojmenovic, Amiya Nayak, "Decentralized Access Control with Anonymous Authentication for Secur-ing Data in Clouds,"IEEE Transactions on Parallel and Distributed Systems, pp. 1045-9219, 2013.

[15] S. Ruj, M. Stojmenovic and A. Nayak, “Privacy Preserving Access Control with Authentication for Securing Data in Clouds”, IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp. 556–563, 2012.

[16] C. Wang, Q. Wang, K. Ren, N. Cao and W. Lou, “Toward Secure and Dependable Storage Services in Cloud Computing”, IEEE T. Services Computing, vol. 5, no. 2, pp. 220–232, 2012.

[17] J. Li, Q. Wang, C. Wang, N. Cao, K. Ren, and W. Lou, “Fuzzy keyword search over encrypted data in cloud computing,” in IEEE INFOCOM. , pp. 441–445, 2010.

[18] S. Kamara and K. Lauter, “Cryptographic cloud storage,” in Financial Cryptography Workshops, ser. Lecture Notes in Com-puter Science, vol. 6054. Springer, pp. 136–149, 2010.

[19] H. Li, Y. Dai, L. Tian, and H. Yang, “Identity-based authentication for cloud computing,” in CloudCom, ser. Lecture Notes in Computer Science, vol. 5931. Springer, pp. 157–166, 2009.

[20] C. Gentry, “A fully homomorphic encryption scheme,” Ph.D. dissertation, Stanford University, 2009, http://www.crypto.stanford.edu/craig.

[21] A.-R. Sadeghi, T. Schneider, and M. Winandy, “Token-based cloud computing,” in TRUST, ser. Lecture Notes in Computer Science, vol. 6101. Springer, pp. 417–429, 2010.

[22] R. K. L. Ko, P. Jagadpramana, M. Mowbray, S. Pearson, M. Kirchberg, Q. Liang, and B. S. Lee, “Trustcloud: A framework for accountability and trust in cloud computing,” HP Technical Report HPL- 2011-38. Available at http://www.hpl.hp.com/techreports/2011/HPL-2011- 38.html.

[23] R. Lu, X. Lin, X. Liang, and X. Shen, “Secure Provenance: The Essential of Bread and Butter of Data Forensics in Cloud Computing,” in ACM ASIACCS, pp. 282–292, 2010.

[24] D. F. Ferraiolo and D. R. Kuhn, “Role-based access controls,” in 15th National Computer Security Conference, 1992.

[25] A B Lewko and B Waters, “Decentralizing attribute based encryption”, springer 2011.