There are two main security concerns in outsourcing computations. One is how to protect the
privacy of the outsourced data, and the other is how to ensure the correctness of the …

We propose a novel primitive called NIVA NIVA that allows the distributed aggregation of
multiple users' secret inputs by multiple untrusted servers. The returned aggregation result …

Aggregating data from multiple sources is often required in multiple applications. In this
paper, we introduce DEVA DEVA, a protocol that allows a distributed set of servers to …

Often clients (eg, sensors, organizations) need to outsource joint computations that are
based on some joint inputs to external untrusted servers. These computations often rely on …

A homomorphic secret sharing (HSS) scheme allows a client to delegate a computation to a
group of untrusted servers while achieving input privacy as long as at least one server is …

When using machine learning classifiers to classify data in cloud computing, it is crucial to
maintain data privacy and ensure the correctness of classification results. To address these …

Large amounts of data are collected by IoT devices, and transmitted wirelessly to cloud
servers for aggregation. These data are often sensitive and need to remain secret …

Homomorphic secret sharing (HSS) allows participants to share their private data between
computing servers for the joint computation of a public function without fully homomorphic …

Cryptographic Tools for Privacy Preservation Page 1 Thesis for The Degree of Doctor of
Philosophy Cryptographic Tools for Privacy Preservation Carlo Brunetta Department of …

This thesis addresses the issue of inflated computational complexity for the verification of
multiple zero-knowledge proofs. More precisely, verification of numerous zero-knowledge …