K-Nearest Neighbor Classification over.pdf

IEEE Transactions on Knowledge and Data Engineering, 2014

Data Mining has wide applications in many areas such as banking, medicine, scientific research and among government agencies. Classification is one of the commonly used tasks in data mining applications. For the past decade, due to the rise of various privacy issues, many theoretical and practical solutions to the classification problem have been proposed under different security models. However, with the recent popularity of cloud computing, users now have the opportunity to outsource their data, in encrypted form, as well as the data mining tasks to the cloud. Since the data on the cloud is in encrypted form, existing privacy preserving classification techniques are not applicable. In this paper, we focus on solving the classification problem over encrypted data. In particular, we propose a secure k-NN classifier over encrypted data in the cloud. The proposed k-NN protocol protects the confidentiality of the data, user's input query, and data access patterns. To the best of our knowledge, our work is the first to develop a fully secure k-NN classifier over encrypted data. Also, we empirically analyze the efficiency of our solution through various experiments.

Data Mining has wide applications in many areas such as medicine, scientific, banking, research and among government agencies. For the past decade, due to the rise of various privacy issues, many theoretical and practical solutions to the classification problem have been proposed under different security models. Classification is one of the commonly used tasks in data mining applications However, with the recent popularity of cloud computing, users now have the opportunity to outsource their data, in encrypted form, as well as the data mining tasks to the cloud. Since the data on the cloud is in encrypted form, existing privacy-preserving classification techniques are not applicable. In this paper, we focus on solving the classification problem over encrypted data. In particular, we propose a secure k-NN classifier over encrypted data in the cloud. The proposed protocol protects the confidentiality of data, privacy of user's input query, and hides the data access patterns. To the best of our knowledge, our work is the first to develop a secure k-NN classifier over encrypted data under the semi-honest model. Also, we empirically analyze the efficiency of our proposed protocol using a real-world dataset under different parameter settings. The proposed system mainly focuses on information security in insurance company. They can encrypt the customer information and stored it in database. When data are encrypted, any data mining tasks becomes very challenging before decrypting data. Classification can apply to the customer records. This protects the customers' sensitive information.

International Journal of Engineering Sciences and Research Technology, 2016

Data mining is a powerful new technique to discover knowledge within the large amount of the data. Also data mining is the process of discovering meaningful new relationship, patterns and trends by passing large amounts of data stored in corpus, using pattern recognition technologies as well as statistical and mathematical techniques. To protect user privacy, various privacy-preserving classification techniques have been proposed over the past decade. The existing techniques are not applicable to outsourced database environments where the data resides in encrypted form on a thirdparty server. This paper proposed a novel privacy-preserving k-NN classification protocol over encrypted data in the cloud. Our protocol protects the confidentiality of the data, user's input query, and hides the data access patterns. We also evaluated the performance of our protocol under different parameter settings.

By the rapid improvement in web help and their popularity, web customers are developing day by day. Hence, there is large and various data. Data Mining has a wide use for the fields of business, medicine, experimental research and among government offices. One of the generally used tasks in data mining applications is Classification. Various professional and possible solutions to the classification problem have occurred introduced in the earlier decades. To overcome the privacy problems, certain clarifications have used various security types. Customers can outsource their data onto encrypted information and the data mining tasks to the cloud. Current privacy preserving classification techniques are not suitable for this encrypted data over the cloud. Securing proper privacy and protection of the data stored, transmitted, prepared, and distributed among the cloud as well as from the users entering such data is one of the big challenges to our current society. Hence, this paper proposes to define the classification problem of encrypted data. A k-NN classifier over encrypted data onto the cloud is proposed here for security reason. This method proposes a protocol to implement the confidentiality of the data onto the cloud protects the privacy of user input query and hides the data access patterns of the cloud. A certain k-NN classifier is the first above the encrypted data onto the semi-honest form. Since developing the performance of SMINn is an essential first step for improving the performance of our PP-k-NN protocol, the alternative and more effective solution than SMINn is studied that extends to different classification algorithms.

Data mining is a powerful new technique to discover knowledge within the large amount of the data. Also data mining is the process of discovering meaningful new relationship, patterns and trends by passing large amounts of data stored in corpus, using pattern recognition technologies as well as statistical and mathematical techniques. To protect user privacy, various privacy-preserving classification techniques have been proposed over the past decade. The existing techniques are not applicable to outsourced database environments where the data resides in encrypted form on a third-party server. This paper proposed a novel privacy-preserving k-NN classification protocol over encrypted data in the cloud. Our protocol protects the confidentiality of the data, user's input query, and hides the data access patterns. We also evaluated the performance of our protocol under different parameter settings.

Machine learning as a service" (MLaaS) in the cloud accelerates the adoption of machine learning techniques. Nevertheless, the externalization of data on the cloud raises a serious vulnerability issue because it requires disclosing private data to the cloud provider. This paper deals with this problem and brings a solution for the K-nearest neighbors (k-NN) algorithm with a homomorphic encryption scheme (called TFHE) by operating on end-to-end encrypted data while preserving privacy. The proposed solution addresses all stages of k-NN algorithm with fully encrypted data, including the majority vote for the class-label assignment. Unlike existing techniques, our solution does not require intermediate interactions between the server and the client when executing the classification task. Our algorithm has been assessed with quantitative variables and has demonstrated its efficiency on large and relevant real-world data sets while scaling well across different parameters on simulated data.

Due to the increasing popularity of cloud computing, organisations have the choice to outsource their large encrypted data content along as well as data mining operations to cloud the environment. Outsourcing data to such a third party cloud environment can compromise the data security as cloud operations and data mining tasks cannot carry out computations without decrypting the data. Hence, already present privacy-preserving data mining techniques are not efficient to address the security and confidentiality problems. In the base paper, a k-NN classification algorithm over secure data under a semi-honest model was developed using a Paillier cryptosystem for public key encryption. The usage of public key cryptosystems has security issues during data transfer in the cloud. In this proposed work, we focus on solving the k-NN problem over secure encrypted data by proposing a privacy preserving k-nearest neighbour classification on encrypted information in the cloud using private key for encryption and decryption based on the symmetric AES cryptographic algorithm under the secure multiparty computations for creating a complete homomorphic encryption (CHE) scheme which results in the reduction of space requirement and processing time. Also, we aim to apply the same PPk-NN classification over encrypted images. The proposed protocol hides the input query and data access patterns of the users and also preserves the confidentiality of text and image data.Finally, we present a practical analysis of the efficiency and security performance of our proposed protocol for application in a Life insurance firm where the clients are classified according to their risk-level.

Data Mining has wide use in many fields such as financial, medication, medical research and among govt. departments. Classification is one of the widely applied works in data mining applications. For the past several years, due to the increase of various privacy problems, many conceptual and realistic alternatives to the classification issue have been suggested under various protection designs. On the other hand, with the latest reputation of cloud processing, users now have to be able to delegate their data, in encoded form, as well as the information mining task to the cloud. Considering that the information on the cloud is in secured type, current privacy-preserving classification methods are not appropriate. In this paper, we concentrate on fixing the classification issue over encoded data. In specific, we recommend a protected k-classifier over secured data in the cloud. The suggested protocol defends the privacy of information, comfort of user's feedback query, and conceals the information access styles. To the best of our information, our task is the first to create a protected k-classifier over secured data under the semi-honest model. Also, we empirically evaluate the performance of our suggested protocol utilizing a real-world dataset under various parameter configurations. To secure user privacy, numerous privacy-preserving category methods have been suggested over the past several years. The current methods are not appropriate to contracted database surroundings where the information exists in secured form on a third-party server

International Journal of Scientific Research in Science, Engineering and Technology, 2022

Cloud Computing stores the data in encrypted form. Classification of the data is required in many machine learning applications, so in the field of cloud computing, classification of the encrypted data is one of the major challenges. Extracting the class dependent features from the encrypted data and using these features in a well-known K-NN classifier can be used to classify the encrypted data at the cloud. In the proposed work we have encrypted and data extracted the correlation coefficient between the two or more variables and feeding it into the K-NN classifier and classifying the data. We have calculated the precision, recall, F1 score and accuracy of the proposed system and evaluated the performance of it with SVM and Naïve Byes classifiers.

Data mining has large variety of real time appliance in several fields such as financial, shopping, telecommunication, biological, and between government agencies. Classification is the one of the major task in data mining. For the past few years, due to the increase in various privacy problem, many theoretical and possible solution to the classification problem have been proposed below different sureness model. The data in the data mining are in encrypted form, existing privacy preserving classification system are not related. Since the data on the data mining is in encrypted form, existing privacy preserving classification technique is not relevant. In this paper, we focus on solving the classification problem over encrypted data. In exacting, we propose a secure k-NN classifier over encrypted data in the data mining approach is very efficient technique. The proposed k-NN protocol protects the privacy of the data, user's input query analysis, and data access pattern. Our work is the first to develop a secure k-NN classifier over encrypted data under the standard with XOR encryption algorithm. To provide enhanced security, a secure kNN protocol that protects the privacy of the data, user's input query analysis, and data access patterns. Also, we empirically analyze the efficiency of our protocols during various experiments. These results specify that our secure protocol is very efficient on the user end, and this light weight method allows a user to use any mobile device to perform the kNN query.