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Nt1310 Unit 1 Literature Review

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2.2. RELATED WORK 2. 2.1. SECURE k-NEAREST NEIGHBOR TECHNIQUES Retrieving the k-Nearest Neighbors to a given query (q) is one of the most fundamental problems in many application domains such as similarity search, pattern recognition, and data mining. In the literature, many techniques have been proposed to address the SkNN problem, which can be classified into two categories based on whether the data are encrypted or not: centralized and distributed. Centralized Methods: In the centralized methods, the data owner is assumed to outsource his/her database and DBMS functionalities (e.g., kNN query) to an untrusted external service provider, which manages the data on behalf of the data owner, where only the trusted users are allowed to query the …show more content…

They addressed the SkNN problem under the following setting: the client has the ciphertexts of all data points in database T and the encryption function of T, whereas the server has the decryption function of T and some auxiliary information regarding each data point. Both methods [51, 98], however, are not secure because they are vulnerable to chosen-plaintext attacks. All the above methods also leak data access patterns to the server. Recently, Yao et al. [99] proposed a new SkNN method based on partition-based a Secure Voronoi Diagram (SVD). Instead of asking the cloud to retrieve the exact kNN, they required the cloud to retrieve a relevant encrypted partitionEpk(G) for Epk(T)such that G is guaranteed to contain the k-nearest neighbors of q. This work, however, solves the SkNN problem accurately by letting the cloud retrieve the exact k-Nearest Neighbors of q (in encrypted form). Additionally, most of the computations during the query processing step in [51, 99, 105] are performed locally by the end-user. That conflicts with the purpose of outsourcing the DataBase Management System (DBMS) functionalities to the cloud. Furthermore, the protocol in secure nearest neighbor revisited[99] leaks data access patterns, such …show more content…

In the past decade, a number of PPDM techniques have been proposed to facilitate users in performing data mining tasks in privacy-sensitive environments. Agrawal and Srikant [3], as well as Lindell and Pinkas [63], were the first to introduce the notion of privacy-preserving under data mining applications. Existing PPDM techniques can be classified into two broad categories: data perturbation and data distribution. Data Perturbation Methods: With these methods, values of individual data records are perturbed by adding random noise in such a way that the distribution of the perturbed data look very deferent from that of the actual data. After such a transformation, the perturbed data is sent to the Miner to perform the desired data mining tasks. Agrawal and Srikant [3] proposed the first data perturbation technique that could be used to build a decision-tree classifier. A number of randomization-based methods were later proposed [6, 33, 34, 73, 104]. Data perturbation techniques are not, however, applicable to semantically- secure encrypted data. They also fail to produce accurate data mining results due to the addition of statistical noises to the data. Data Distribution Methods: These methods assume that the dataset is partitioned eitherhorizontallyorverticallyanddistributedacrossdifferentparties. The parties

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