Amir Daneshgar

Vahid Jahandideh, Amir Daneshgar, Mahmoud Salmasizadeh

We study masked implementation's security when an adversary randomly probes each of its internal variables, intending to recover non-trivial knowledge about its secrets. We introduce a novel metric called Secret Recovery Probability (SRP) for assessing the informativeness of the probing leakages about the masked secrets. To evaluate SRP, our starting point is to describe the relations of the intermediate variables with a parity equation system where the target secret is an unknown of this system ...

Amir Daneshgar, Fahimeh Mohebbipoor

We follow two main objectives in this article. On the one hand, we introduce a security model called LORBACPA$^+$ for self-synchronized stream ciphers which is stronger than the blockwise LOR-IND-CPA, where we show that standard constructions as delayed CBC or similar existing self-synchronized modes of operation are not secure in this stronger model. Then, on the other hand, following contributions of G.~Millérioux et.al., we introduce a new self-synchronized stream cipher and prove its security in LORBACPA$^+$ model.

Amir Daneshgar, Behrooz Khadem

In this paper, a word based chaotic image encryption scheme for gray images is proposed, that can be used in both synchronous and self-synchronous modes. The encryption scheme operates in a finite field where we have also analyzed its performance according to numerical precision used in implementation. We show that the scheme not only passes a variety of security tests, but also it is verified that the proposed scheme operates faster than other existing schemes of the same type even when using lightweight short key sizes.

Amir Daneshgar, Ramin Javadi, Basir Shariat Razavi

In this paper we propose a graph-based data clustering algorithm which is based on exact clustering of a minimum spanning tree in terms of a minimum isoperimetry criteria. We show that our basic clustering algorithm runs in $O(n \log n)$ and with post-processing in $O(n^2)$ (worst case) time where $n$ is the size of the data set. We also show that our generalized graph model which also allows the use of potentials at vertices can be used to extract a more detailed pack of information as the {\it outlier profile} of the data set. In this direction we show that our approach can be used to define the concept of an outlier-set in a precise way and we propose approximation algorithms for finding such sets. We also provide a comparative performance analysis of our algorithm with other related ones and we show that the new clustering algorithm (without the outlier extraction procedure) behaves quite effectively even on hard benchmarks and handmade examples.