The growth of the internet provides opportunities for cooperative computation, it also requires development of protocols that can accomplish this task among mutually untrusting parties. The aim is to develop methods which ensure both the correct evaluation of the function and privacy of individual inputs. Multiparty Computation protocols help to achieve the aim without using a trusted third par...

Secure function evaluation (SFE) is the process of computing a function (or running an algorithm) on some data, while keeping the input, output and intermediate results hidden from the environment in which the function is evaluated. This can be done using fully homomorphic encryption, Yao's garbled circuits or secure multiparty computation. Applications are manifold, most prominently the outsou...

M scientific and policy investigations require statistical analyses that “integrate” data stored in multiple, distributed databases. For example, a regression analysis on integrated state databases about factors influencing student performance would be more insightful than individual analyses, or complementary to them. Other contexts where the same need arises range from homeland security to en...

Ray Kresman, Advisor Data mining algorithms help reveal hidden information in a repository. Distributed mining algorithms meet this need by distributing data and computation. One of the most important issues of these algorithms is how to safely mine the data. Secure Multiparty Computation (SMC), a framework for safe mining of distributed data, provides some security promises of the computation....

In the setting of secure multiparty computation, a set of mutually distrustful parties wish to securely compute some joint function of their private inputs. The computation should be carried out in a secure way, meaning that the properties privacy, correctness, independence of inputs, fairness and guaranteed output delivery should all be preserved. Unfortunately, in the case of no honest majori...

Multiparty delegated quantum computation (MDQC) allows multiple clients with limited capability to jointly complete a computational task the aid of an untrusted server. But in existing MDQC protocols, verifiability that should verify whether server executed protocol correctly and gave correct results was not handled. Therefore, this paper, we improve typical enable correctness by inserting trap...

In this paper, we formalize the notion of distributed sensitive social networks (DSSNs), which encompasses networks like enmity networks, financial transaction networks, supply chain networks and sexual relationship networks. Compared to the well studied traditional social networks, DSSNs are often more challenging to study, given the privacy concerns of the individuals on whom the network is k...

The great potential of data mining in a networked world cannot be realized without acceptable guarantees that private information will be protected. In theory, general cryptographic protocols for secure multiparty computation enable data mining with privacy preservation that is optimal with respect to the desired end results. However, the performance expense of such general protocols is prohibi...