Indexed In Scopus
  Scopus ID: 21100926589

A Novel and Efficient Detection of Data Leak with Effective Data Allocation Strategies on Cloud

S. Saisree and V. Balaji


With the growth of internet technologies data handling and sharing is inevitable and this is achieved by cloud computing. When a data outsourcer distributes sensitive data to the set of their subscribed and authentic agents, it is always possible that one of the authentic agents may leak the data to an unauthentic third party, this causes the sensitive data leak. Cloud computing plays an important role in data storage and sharing among agents. Sensitive data leak may cause illegitimate data usage among third party agents. When the data outsourcer comes to know that the sensitive data is leaked by witnessing data in the web or other platforms, he must be able to detect the guilt agent, thus he can be able to stop sharing further data and take actions. The proposed work achieves the objective of guilt agent detection and data leak detection by introducing fake data tuples/objects and the method of probability of distribution. The fake object is allocated on runtime dynamically and unique for every agent. The uniqueness of the fake object allows it to detect the agent who leaks the data. There are existing techniques like watermarking and anonymization to outsource data. Though the leak can be detected by the existing techniques the fault agent detection cannot be achieved, moreover the data is modified in some form and may affect the originality of data. This proposed work achieves both goals effectively.

Published on: December 07, 2023
doi: 10.17756/nwj.2023-s4-083
Citation: Saisree S, Balaji V. 2023. A Novel and Efficient Detection of Data Leak with Effective Data Allocation Strategies on Cloud. NanoWorld J 9(S4): S494-S499.

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