Deep learning can achieve higher accuracy than traditional machine learning algorithms in
a variety of machine learning tasks. Recently, privacy-preserving deep learning has drawn …

Blockchain is a disruptive technology applied by academia and industry in a broad spectrum
of applications ranging from cryptocurrency, financial services, IoT, 5G, risk management to …

We introduce and formalize the notion of Verifiable Computation, which enables a
computationally weak client to “outsource” the computation of a function F on various …

Cloud computing emerges as a new computing paradigm that aims to provide reliable,
customized and quality of service guaranteed computation environments for cloud users …

We study a model of fairness in secure computation in which an adversarial party that aborts
on receiving output is forced to pay a mutually predefined monetary penalty. We then show …

We study the problem of computing on large datasets that are stored on an untrusted server.
We follow the approach of amortized verifiable computation introduced by Gennaro, Gentry …

Cloud computing has become an irreversible trend. Together comes the pressing need for
verifiability, to assure the client the correctness of computation outsourced to the cloud …

We study a model of incentivizing correct computations in a variety of cryptographic tasks.
For each of these tasks we propose a formal model and design protocols satisfying our …

Outsourced computations (where a client requests a server to perform some computation on
its behalf) are becoming increasingly important due to the rise of Cloud Computing and the …

Machine Learning as a Service (MLaaS) allows clients with limited resources to outsource
their expensive ML tasks to powerful servers. Despite the huge benefits, current MLaaS …