Collaborative Filtering (CF) is one of the most successful recommendation approaches to cope with information overload in the real world. However, typical CF methods equally treat every user and item, and cannot distinguish the variation of user’s interests across different domains. This violates the reality that user’s interests always center on some specific domains, and the users having similar tastes on one domain may have totally different tastes on another domain. Motivated by the observation, in this paper, we propose a novel Domain-sensitive Recommendation (DsRec) algorithm, to make the rating prediction by exploring the user-item subgroup analysis simultaneously, in which a user-item subgroup is deemed as a domain consisting of a subset of items with similar attributes and a subset of users who have interests in these items. The proposed framework of DsRec includes three components: a matrix factorization model for the observed rating reconstruction, a bi-clustering model for the user-item subgroup analysis, and two regularization terms to connect the above two components into a unified formulation. Extensive experiments on Movielens-100K and two real-world product review datasets show that our method achieves the better performance in terms of prediction accuracy criterion over the state-of-the-art methods.
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.
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.
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.
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.
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.
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
Domain-Sensitive Recommendation with User-Item Subgroup Analysis Domain-Sensitive Recommendation with User-Item Subgroup Analysis Domain-Sensitive Recommendation with User-Item Subgroup Analysis Domain-Sensitive Recommendation with User-Item Subgroup Analysis