Efficient Delegated Private Set Intersection on Outsourced Private Datasets - 2017

PROJECT TITLE :

Efficient Delegated Private Set Intersection on Outsourced Private Datasets - 2017

ABSTRACT:

Private set intersection (PSI) is an important cryptographic protocol that has several universe applications. As Cloud Computing power and popularity have been swiftly growing, it is now desirable to leverage the cloud to store personal datasets and delegate PSI computation to it. Although a collection of efficient PSI protocols have been designed, none support outsourcing of the datasets and the computation. During this paper, we have a tendency to propose two protocols for delegated PSI computation on outsourced private datasets. Our protocols have a distinctive combination of properties that make them particularly appealing for a Cloud Computing setting. Our initial protocol, O-PSI, satisfies these properties by using additive homomorphic encryption and purpose-value polynomial representation of a set. Our second protocol, EO-PSI, is mainly primarily based on a hash table and purpose-worth polynomial representation and it does not require public key encryption; meanwhile, it retains all the desirable properties and is much a lot of economical than the first one. We tend to also offer a proper security analysis of the 2 protocols within the semi-honest model and we have a tendency to analyze their performance utilizing prototype implementations we tend to have developed. Our performance analysis shows that EO-PSI scales well and is also more efficient than similar state-of-the-art protocols for giant set sizes.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here