BibTeX:

@article{IJIRSTV3I2146,
     title={Generating a Framework for Secure Multiparty Computation with Server and Multiple Clients},
     author={Asima Ummul Fatah Sarmast and Prof. Sujata Mallapur},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={3},
     number={2},
     pages={499--501},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV3I2146.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

A generic framework, with the help of a preprocessing phase that is independent of the users inputs, that allows an arbitrary number of users to securely utilizing a computation of two non-collaborating external servers. Here approach is to shown provably secure in an adversarial model where one of the servers may arbitrarily deviate from the protocol specification, as well as employ an arbitrary number of dummy users. Recommendation systems consist of a processor together with a multitude of users, where the processor provides recommendations to requesting users, which are deduced from personal ratings that were initially submitted by all users. It is easy to see that, in a non-cryptographic setup of such a system, the processor is both able to learn all data submitted by the users, and spoof arbitrary, incorrect recommendations. Use these techniques to implement a secure recommender system based on collaborative filtering that becomes more secure, and more efficient than previously known implementations of such systems, when the preprocessing efforts are remove. We suggest different alternatives for preprocessing, and discuss their merits and demerits.

Secure Multi party computation, Malicious Model, Client-Server, Secrete Shared, Pre-processing recommender system