Paper Title
Privacy Preserving For Expertise Data Using K-Anonymity Technique To Advice The Farmers

Privacy preserving for an expertized dataset using security techniques to advice the farmers. Earlier protects the privacy of the data by perturbing the data through a random process. The second approach uses cryptographic techniques to perform secure multi-party computation. In this paper, we propose privacy preservation for a data classification using Kanonymity algorithm. This paper includes a protection model named k-anonymity and a set of accompanying policies for deployment. K-anonymity Protection means the information for each person contained in the release cannot be distinguished from at least k-1 individuals whose information also appears in the release. Finally, we show that our method contributes new and efficient ways to anonymize data and preserve patterns during anonymization .This intelligent system using decision tree algorithm. The decision trees generated by ID3 can be used for classification.ID3 builds decision trees from a set of training data in the same way as ID3, using the concept of information entropy. Using this ID3 Algorithm we developed a new ‘Privacy Preserving for expertise data’. This system is mainly aimed for identifying the diseases and disease management in Lemon fruits and Lemon plants to advise the farmers through online in the villages to obtain standardized yields and provide protection. The present advisory system is also used privacy preserving technique.