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IJSTR >> Volume 9 - Issue 1, January 2020 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Detection Of Data Leakage In Cloud Storages

[Full Text]



Naresh Vurukonda, Allu Venkata Dattatreya Reddy, Gutta Chiranjeevi, Kancharla Raviteja



water marking,guilt agent, fake object, probability, automation



Leakage of sensitive data may leads to the loss of confidential and integrity. Some of the data may be leaked and found on web or untrusted users. Distributor have to take upon these situations in order to maintain data confidentiality and ensure a safe data transaction. Many small business authorities have data leak issues via internet or other means. We would like to propose a alternative methodology to implement in real world and it is different from traditional methods. Traditional methods contain “watermarking” and in some cases we can also inject “realistic but fake” data records to further improve our chances of detecting leakage and identifying the guilty party. But this also will not work if the guilt agent knows the fake objects. So the other method for getting the guilt agents is to be determined. Many methods have been in existence but every method is being override by other means using complex methodologies and by various combinations of the algorithms. These complex methods would secure much better than older ones. We are finding the agents by taking the parameters like how much time he is spending in the data, how many times he opened that file etc.... we can find the probability if the probability is more than the threshold value then we can conclude that the agent had compromised. In this model we use the previous methods knowledge to predict the agents or to over come in the solution.



[1] Reddy, G. Venkatakoti et al. “A review on active data access control for multi-authority cloud storage systems with users.” 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC) (2017): 262-266.
[2] Vurukonda, Naresh & Rao, Dr. B.Thirumala. (2016). A Study on Data Storage Security Issues in Cloud Computing. Procedia Computer Science. 92. 128-135. 10.1016/j.procs.2016.07.335.
[3] Abbas, Assad et al. “A cloud based health insurance plan recommendation system: A user centered approach.” Future Generation Comp. Syst. 43-44 (2015): 99-109.
[4] P. Mell, T. Grance, The NIST definition of cloud computing (draft), NIST Special Publ. 800 (145) (2011) 7.
[5] Vurukonda, Naresh & Rao, Dr. B.Thirumala. (2016). A Study on Data Storage Security Issues in Cloud Computing. Procedia Computer Science. 92. 128-135. 10.1016/j.procs.2016.07.335.
[6] Chavan, Jaymala and Priyanka Desai. “Relational Data Leakage Detection using Fake Object and Allocation Strategies.” (2013).
[7] Papadimitriou, Panagiotis and Hector Garcia-Molina. “A Model for Data Leakage Detection.” 2009 IEEE 25th International Conference on Data Engineering (2009): 1307-1310.
[8] Wakhare Yashwant R & B. M. Patil, “Data Leakage Detection with K-Anonymit Algorithm”
[9] DATA LEAK DETECTION Research article by Ms. N. Bangar Anjali1, Ms. P. Rokade Geetanjali2, Ms. Patil Shivlila3, Ms. R. Shetkar Swati4, Prof. N B Kadu5 from “ijsmc”.
[10] Periyasamy, A R. Pon and E. Thenmozhi. “Data Leakage Detection and Data Prevention Using Algorithm.” (2017).
[11] Review Paper on Dynamic Mechanisms of Data Leakage Detection and Prevention by shivkumar tuppada, Muneswar M M S, Dr. Rajasekhar patil.
[12] Guevara, César et al. “Data leakage detection algorithm based on task sequences and probabilities.” Knowl.-Based Syst. 120 (2017): 236-246.
[13] Rajasekaran, M & Gupta, Amisha & Sharma, Padmini. (2018). Data Leakage Prevention and Detection System. International Journal of Engineering & Technology. 7. 366. 10.14419/ijet.v7i3.12.16108.