Searchable encrypted systems enable privacy-preserving keyword search on encrypted
data. Symmetric systems achieve high efficiency (eg, sublinear search), but they mostly …

Parallel computation is an important aspect of multi-party computation, not only in terms of
improving efficiency, but also in terms of providing privacy for computation involving …

A private decision tree evaluation (PDTE) protocol allows a feature vector owner (FO) to
classify its data using a tree model from a model owner (MO) and only reveals an inference …

We introduce Oblivious Parallel Stateful Computation (OPSC), a form of secure multi-party
computation (MPC) tailored for stateful machine computation models emphasizing parallel …

Card-based cryptography allows us to securely compute arbitrary functions using a deck of
physical cards. Its performance is mainly measured by the number of used cards and …

A secret-shared shuffle (SSS) protocol permutes a secret-shared vector using a random
secret permutation. It has found numerous applications, however, it is also an expensive …

Decision tree evaluation is extensively used in machine learning to construct accurate
classification models. Often in the cloud-assisted communication paradigm cloud servers …

Card-based cryptography allows us to securely compute arbitrary functions using a deck of
physical cards. Its performance is mainly measured by the number of used cards and …

In this paper, we propose memory-and round-efficient protocols for securely evaluating
arithmetic primitives. We focus on secure two-party computation over the ring ℤ2k that …

Secure two-party computation is a cryptographic technique that enables two parties to
compute a function jointly while keeping each input secret. It is known that most functions …