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Survey Paper | Computer Science & Engineering | India | Volume 3 Issue 12, December 2014
Privacy Preserving ANN Over Cloud
Babaso Shinde | Ajay Gupta [4]
Abstract: Backpropagation is very good and effective method for learning neural networking. This method is widely used various application. As compare to learning with local data set the collaborative learning is very effective to learn new things. The collaborative infrastructure like cloud computing the participating parties carries out learning not only their own data set, but also on other data set. The cloud computing is more convenient than ever for user across the internet. The user internet can shared all data without knowing with each other. Beside of this advantage there one crucial issue pertaining to the internet-wide collaborative neural network learning is the protection of the data privacy for each participant. The participant in the cloud computing is from different trust domain and they may not Want to release their private data set which may contain proprietary information to anybody else. The solution shall be effective and scalable enough to support an random number of participants each processing arbitrarily participant data set. Co-operative data sharing is an important aspect that is emerging heavily in cloud computing. This comes with a large risk of data leakage from the cloud. Thus a need for encrypting data before storing on cloud becomes almost mandatory. But, in a multi owner data access structure it is important for clients to find mining results, to make any system function effortlessly. This paper supports architecture for storing data in an encrypted manner on the server, yet making it feasible to apply ANN for mining on encrypted data with an encrypted query, which makes it impossible for a curious cloud owner to find and meaning of data, not even from the query.
Keywords: Privacy reserving, learning, neural network, back-propagation, cloud computing
Edition: Volume 3 Issue 12, December 2014,
Pages: 2715 - 2717