We propose TrustSVD, a trust-based matrix factorization technique for recommendations. TrustSVD integrates multiple information sources into the recommendation model in order to reduce the data sparsity and cold start problems and their degradation of recommendation performance. An analysis of social trust data from four real-world data sets suggests that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. TrustSVD therefore builds on top of a state-of-the-art recommendation algorithm, SVD++ (which uses the explicit and implicit influence of rated items), by further incorporating both the explicit and implicit influence of trusted and trusting users on the prediction of items for an active user. The proposed technique is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that TrustSVD achieves better accuracy than other ten counterparts recommendation techniques.

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 for owners.

System Configuration:

HARDWARE REQUIREMENTS:

Hardware – Pentium

Speed – 1.1 GHz

RAM – 1GB

Hard Disk – 20 GB

Key Board – Standard Windows Keyboard

Mouse – Two or Three Button Mouse

Monitor – SVGA

A Novel Recommendation Model Regularized with User Trust and Item Ratings