The privacy preserving data mining is playing crucial role act as rising technology to perform various data mining operations on private data and to pass on data in a secured way to protect sensitive data. Many types of technique such as randomization, secured sum algorithms and k-anonymity have been suggested in order to execute privacy preserving data mining. In this survey paper, on current researches made on privacy preserving data mining technique with fuzzy logic, neural network learning, secured sum and various encryption algorithm is presented. This will enable to grasp the various challenges faced in privacy preserving data mining and also help us to find best suitable technique for various data environment.

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Abstract-In this paper we analyse the role of some of the building blocks in SHA-256. We show that the disturbance correction strategy is applicable to the SHA-256 architecture and we prove that functions Σ, σ are vital for the security of SHA-256 by showing that for a variant without them it is possible to nd collisions with complexity 2 64 hash operations. As a step towards an analysis of the full function, we present the results of our experiments on Hamming weights of expanded messages for different variants of the message expansion and show that there exist low-weight expanded messages for XOR-linearised variants.Index Terms-A SHA-256, Hamming weight, collision