The k-nearest neighbor approach (k-NN) has been extensively used as a powerful non-parametric technique in many scientific and engineering applications. However, this approach incurs a large computational cost. Hence, this issue has become an active research field. In this work, a novel k-NN approach based on various-widths clustering, named kNNVWC, to efficiently find k-NNs for a query object from a given data set, is presented. kNNVWC does clustering using various widths, where a data set is clustered with a global width first and each produced cluster that meets the predefined criteria is recursively clustered with its own local width that suits its distribution. This reduces the clustering time, in addition to balancing the number of produced clusters and their respective sizes. Maximum efficiency is achieved by using triangle inequality to prune unlikely clusters. Experimental results demonstrate that kNNVWC performs well in finding k-NNs for query objects compared to a number of k-NN search algorithms, especially for a data set with high dimensions, various distributions and large size.

 

Exisiting Systems

                    There is only one authority responsible for attribute management and key distribution. This only-one-authority scenario can bring a single-point bottleneck on both security and performance. Once the authority is compromised, an adversary can easily obtain the only-one-authority’s master key, then he/she can generate private keys of any attribute subsetto decrypt the specific encrypted data. Crash or offline of a specific authority will make that private keys of all attributes in attribute subset maintained by this authority cannot be generated and distributed, which will still influence the whole system’s effective operation.

Advantages:

        This only-one-authority scenario can bring a single-point bottleneck on both security and performance.

These CP-ABE schemes are still far from being widely used for access control in public cloud storage.

 

 

 

 

 

 

Disadvantages:

          Crash or offline of a specific authority will make that private keys of all attributes in attribute subset maintained by this authority cannot be generated and distributed, which will still influence the whole system’s effective operation.

the access structure is not flexible enough to satisfy complex environments. Subsequently,much effort has been made to deal with the disadvantages  in the early schemes.

 

MODULE DESCRIPTION:

In this  project, A Robust  and  Verifiable Threshold Multi-Authority

Access Control System in Public Cloud Storage have three modules .

  • User module
  • MultiauthorityAccess control
  • Public cloud storage.

 

 

 

 

User Module:

          In this module, Users are having authentication and security to access the detail which is presented in the system. Before accessing or searching the details user should have the account in that otherwise they should register first.

Multi-authority Access control:

          We conduct a threshold multi-authority CP-ABE access control scheme for

public cloud storage, named TMACS, in which multiple authorities jointly manage a uniform attribute set

To the best of our knowledge, we are the first to design a multiauthority access control architecture to deal with the problem.

To satisfy this hybrid scenario, we conduct a hybrid multi-authority access control scheme, by combining the traditional multi-authority scheme   with our proposed TMACS.

Public Cloud Storage:

          Cloud storage is an important service of cloud computing  which provides services for data owners to outsource data to store in cloud via Internet.

The cloud server is always online and managed by the cloud provider. Usually, the cloud server and its provider is assumed “honest-but-curious”.

The cloud server does nothing but provide a platform for owners storing and sharing their encrypted data.The cloud server doesn’t conduct data access control

kNNVWC: An Efficient k-Nearest Neighbors Approach Based on Various-Widths Clustering kNNVWC: An Efficient k-Nearest Neighbors Approach Based on Various-Widths Clustering kNNVWC: An Efficient k-Nearest Neighbors Approach Based on Various-Widths Clustering kNNVWC: An Efficient k-Nearest Neighbors Approach Based on Various-Widths Clustering kNNVWC: An Efficient k-Nearest Neighbors Approach Based on Various-Widths Clustering