Automatic detection of DoS vulnerabilities of cryptographic protocols

In this article the subject of DoS vulnerabilities of cryptographic key establishment and authentication protocols is discussed. The system for computer-aided DoS protocol resistance analysis, which employs the Petri nets formalism and Spin model-checker, is presented

Effective reduction of cryptographic protocols specification for model-checking with Spin

In this article a practical application of the Spin model checker for verifying cryptographic protocols was shown. An efficient framework for specifying a minimized protocol model while retaining its functionality was described. Requirements for such a model were discussed, such as powerful adversary, multiple protocol runs and a way of specifying validated properties as formulas in temporal logic

The High-Level Practical Overview of Open-Source Privacy-Preserving Machine Learning Solutions

This paper aims to provide a high-level overview of practical approaches to machine-learning respecting the privacy and confidentiality of customer information, which is called Privacy-Preserving Machine Learning. First, the security approaches in offline-learning privacy methods are assessed. Those focused on modern cryptographic methods, such as Homomorphic Encryption and Secure Multi-Party Computation, as well as on dedicated combined hardware and software platforms like Trusted Execution Environment - Intel® Software Guard Extensions (Intel® SGX). Combining the security approaches with different machine learning architectures leads to our Proof of Concept in which the accuracy and speed of the security solutions will be examined. The next step was exploring and comparing the Open-Source Python-based solutions for PPML. Four solutions were selected from almost 40 separate, state-of-the-art systems: SyMPC, TF-Encrypted, TenSEAL, and Gramine. Three different Neural Network architectures were designed to show different libraries’ capabilities. The POC solves the image classification problem based on the MNIST dataset. As the computational results show, the accuracy of all considered secure approaches is similar. The maximum difference between non-secure and secure flow does not exceed 1.2%. In terms of secure computations, the most effective Privacy-Preserving Machine Learning library is based on Trusted Execution Environment, followed by Secure Multi-Party Computation and Homomorphic Encryption. However, most of those are at least 1000 times slower than the non-secure evaluation. Unfortunately, it is not acceptable for a real-world scenario. Future work could combine different security approaches, explore other new and existing state-of-the-art libraries or implement support for hardware-accelerated secure computation

Confidential greedy graph algorithm

Confidential algorithm for the approximate graph vertex covering problem is presented in this article. It can preserve privacy of data at every stage of the computation, which is very important in context of cloud computing. Security of~our solution is based on fully homomorphic encryption scheme. The time complexity and the security aspects of considered algorithm are described

Block cipher based Public Key Encryption viaIndistinguishability Obfuscation

The article is devoted to generation techniques of thenew public key crypto-systems, which are based on applicationof indistinguishability obfuscation methods to selected privatekey crypto-systems. The techniques are applied to symmetrickey crypto-system and the target system is asymmetric one.As an input for our approach an implementation of symmetricblock cipher with a given private-key is considered. Differentobfuscation methods are subjected to processing. The targetsystem would be treated as a public-key for newly createdpublic crypto-system. The approach seems to be interestingfrom theoretical point of view. Moreover, it can be useful forinformation protection in a cloud-computing model

ID-based, proxy, threshold signature scheme

We propose the proxy threshold signature scheme with the application of elegant construction of verifiable delegating key in the ID-based infrastructure, and also with the bilinear pairings. The protocol satisfies the classical security requirements used in the proxy delegation of signing rights. The description of the system architecture and the possible application of the protocol in edge computing designs is enclosed

Implementation of Large Neural Networks Using Decomposition

The article presents methods of dealing with huge data in the domain of neural networks. The decomposition of neural networks is introduced and its efficiency is proved by the authors’ experiments. The examinations of the effectiveness of argument reduction in the above filed, are presented. Authors indicate, that decomposition is capable of reducing the size and the complexity of the learned data, and thus it makes the learning process faster or, while dealing with large data, possible. According to the authors experiments, in some cases, argument reduction, makes the learning process harder

An Efficiency Measure of FPGA Based Logic Synthesis Tools

In FPGA - based designs, the number of LOgic Cells (LCs) needed is an important criterion to judge whether a desing is good or not. But the total number of LCs needed to implement a circuit differs vastly from tool to tool. Normally, vendor software use more LCs than the theoretical maximum needed by functional decomposition to implement a circuit. Academic software uses less number of LCs. So far, we are aware of any technique that would give a quantitative measure to judge the comparable silicon area efficiency of a logic synthesis tool. This paper presents a technique to calculate the minmax number of logic cells (Q) which are necedssary to implement a logic circuit