Mahmoud Salmasizadeh

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 ...

Alireza Kavousi, Javad Mohajeri, Mahmoud Salmasizadeh

Private Set Intersection (PSI) is a vital cryptographic technique used for securely computing common data of different sets. In PSI protocols, often two parties hope to find their common set elements without needing to disclose their uncommon ones. In recent years, the cloud has been playing an influential role in PSI protocols which often need huge computational tasks. In 2017, Abadi et al. introduced a scheme named EO-PSI which uses a cloud to pass on the main computations to it and does not include any public-key operations. In EO-PSI, parties need to set up secure channels beforehand; otherwise, an attacker can easily eavesdrop on communications between honest parties and find private information. This paper presents an improved EO-PSI scheme which has the edge on the previous scheme in terms of privacy and complexity. By providing possible attacks on the prior scheme, we show the necessity of using secure channels between parties. Also, our proposed protocol is secure against passive attacks without having to have any secure channels. We measure the protocol's overhead and show that computational complexity is considerably reduced and also is fairer compared to the previous scheme.