João Soares Resende

Abstract

Abstract

Abstract
There are many cryptographic protocols in the literature that are scientifically and mathematically sound. By extension, cryptography today seeks to respond to numerous properties of the communication process beyond confidentiality (secrecy), such as integrity, authenticity, and anonymity. In addition to the theoretical evidence, implementations must be equally secure. Due to the ever-increasing intrusion from governments and other groups, citizens are now seeking alternatives ways of communication that do not leak information. In this paper, we analyze multiparty computation (MPC), which is a sub-field of cryptography with the goal of creating methods for parties to jointly compute a function over their inputs while keeping those inputs private. This is a very useful method that can be used, for example, to carry out computations on anonymous data without having to leak that data. Thus, due to the importance of confidentiality in this type of technique, we analyze active and passive attacks using complexity measures (compression and entropy). We start by obtaining network traces and syscalls, then we analyze them using compression and entropy techniques. Finally, we cluster the traces and syscalls using standard clustering techniques. This approach does not need any deep specific knowledge of the implementations being analyzed. This paper presents a security analysis for four MPC frameworks, where three were identified as insecure. These insecure libraries leak information about the inputs provided by each party of the communication. Additionally, we have detected, through a careful analysis of its source code, that SPDZ-2’s secret sharing schema always produces the same results. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

Abstract
The growth of the Internet of Things (IoT) is raising significant impact in several contexts, e.g., in cities, at home, and even attached to the human body. This digital transformation is happening at a high pace and causing a great impact in our daily lives, namely in our attempt to make cities smarter in an attempt to increase their efficiency while reducing costs and increasing safety. However, this effort is being supported by the massive deployment of sensors throughout cities worldwide, leading to increase concerns regarding security and privacy. While some of these issues have already been tackled, device authentication remains without a viable solution, specially when considering a resilient decentralized approach that is the most suitable for this scenario, as it avoids some issues related to centralization, e.g., censorship and data leakage or profit from corporations. The provisioning is usually an arduous task that encompasses device configuration, including identity and key provisioning. Given the potential large number of devices, this process must be scalable and semi-autonomous, at least. This work presents a novel approach for provisioning IoT devices that adopts an architecture where other device acts as a manager that represents a CA, allowing it to be switched on/off during the provisioning phase to reduce single point of failure (SPOF) problems. Our solution combines One Time Password (OTP) on a secure token and cryptographic algorithms on a hybrid authentication system. © 2019, Springer Nature Switzerland AG.

Abstract
Security and privacy concerns are challenging the way users interact with devices. The number of devices connected to a home or enterprise network increases every day. Nowadays, the security of information systems is relevant as user information is constantly being shared and moving in the cloud; however, there are still many problems such as, unsecured web interfaces, weak authentication, insecure networks, lack of encryption, among others, that make services insecure. The software implementations that are currently deployed in companies should have updates and control, as cybersecurity threats increasingly appearing over time. There is already some research towards solutions and methods to predict new attacks or classify variants of previous known attacks, such as (algorithmic) information theory. This survey combines all relevant applications of this topic (also known as Kolmogorov Complexity) in the security and privacy domains. The use of Kolmogorov-based approaches is resource-focused without the need for specific knowledge of the topic under analysis. We have defined a taxonomy with already existing work to classify their different application areas and open up new research questions.