Secure Architectures for Mobile Applications

The paper presents security issues and architectures for mobile applications and GSM infrastructure. The article also introduces the idea of a new secure architecture for an inter-sector electronic wallet used in payments - STP4EW (Secure Transmission Protocol for Electronic Wallet)secure architecture, m-application, smart-cards, 3G Mobile

Foundations for Designing Secure Architectures

AbstractDeveloping security-critical systems is difficult and there are many well-known examples of security weaknesses exploited in practice. In particular, so far little research has been performed on the soundly based design of secure architectures, which would be urgently needed to develop secure systems reliably and efficiently. In this abstract, we sketch some research on a sound methodology supporting secure architecture design. We give an overview over an extension of UML, called UMLsec, that allows expressing security-relevant information within the diagrams in an architectural design specification. We define foundations for secure architectural design patterns. We present tool-support which has been developed for the UMLsec secure architecture approach

Memory-Based High-Level Synthesis Optimizations Security Exploration on the Power Side-Channel

High-level synthesis (HLS) allows hardware designers to think algorithmically and not worry about low-level, cycle-by-cycle details. This provides the ability to quickly explore the architectural design space and tradeoffs between resource utilization and performance. Unfortunately, security evaluation is not a standard part of the HLS design flow. In this article, we aim to understand the effects of memory-based HLS optimizations on power side-channel leakage. We use Xilinx Vivado HLS to develop different cryptographic cores, implement them on a Spartan-6 FPGA, and collect power traces. We evaluate the designs with respect to resource utilization, performance, and information leakage through power consumption. We have two important observations and contributions. First, the choice of resource optimization directive results in different levels of side-channel vulnerabilities. Second, the partitioning optimization directive can greatly compromise the hardware cryptographic system through power side-channel leakage due to the deployment of memory control logic. We describe an evaluation procedure for power side-channel leakage and use it to make best-effort recommendations about how to design more secure architectures in the cryptographic domain

Secure architectures for pairing based public key cryptography

Along with the growing demand for cryptosystems in systems ranging from large servers to mobile devices, suitable cryptogrophic protocols for use under certain constraints are becoming more and more important. Constraints such as calculation time, area, efficiency and security, must be considered by the designer. Elliptic curves, since their introduction to public key cryptography in 1985 have challenged established public key and signature generation schemes such as RSA, offering more security per bit. Amongst Elliptic curve based systems, pairing based cryptographies are thoroughly researched and can be used in many public key protocols such as identity based schemes. For hardware implementions of pairing based protocols, all components which calculate operations over Elliptic curves can be considered. Designers of the pairing algorithms must choose calculation blocks and arrange the basic operations carefully so that the implementation can meet the constraints of time and hardware resource area. This thesis deals with different hardware architectures to accelerate the pairing based cryptosystems in the field of characteristic two. Using different top-level architectures the hardware efficiency of operations that run at different times is first considered in this thesis. Security is another important aspect of pairing based cryptography to be considered in practically Side Channel Analysis (SCA) attacks. The naively implemented hardware accelerators for pairing based cryptographies can be vulnerable when taking the physical analysis attacks into consideration. This thesis considered the weaknesses in pairing based public key cryptography and addresses the particular calculations in the systems that are insecure. In this case, countermeasures should be applied to protect the weak link of the implementation to improve and perfect the pairing based algorithms. Some important rules that the designers must obey to improve the security of the cryptosystems are proposed. According to these rules, three countermeasures that protect the pairing based cryptosystems against SCA attacks are applied. The implementations of the countermeasures are presented and their performances are investigated

Post-Quantum Secure Architectures for Automotive Hardware Secure Modules

The rapid development of information technology in the automotive industry has driven increasing requirements on incorporating security functionalities in the in-vehicle architecture, which is usually realized by adding a Hardware Secure Module (HSM) in the Electronic Central Unit (ECU). Therefore, secure communications can be enforced by carrying out secret cryptographic computations within the HSM by use of the embedded hardware accelerators. However, there is no common standard for designing the architecture for an automotive HSM. A future design of a common automotive HSM is desired by the automotive industry which not only fits to the increasing performance demand, but also further defends against future attacks by attackers exploiting large-scale quantum computers. The arrival of future quantum computers motivates the investigation into post-quantum cryptography (PQC), which will retain the security of an HSM in the future. We analyzed the candidates in NIST\u27s PQC standardization process, and proposed new sets of hardware accelerators for the future generation of the automotive HSMs. Our evaluation results show that building a post-quantum secure automotive HSM is feasible and can meet the hard requirements imposed by a modern vehicle ECU

Privacy-Preserving Image Classification Using Convolutional Neural Networks

The process of image classification using convolutional neural networks (CNNs) often relies on access to large, annotated datasets and the use of cluster or cloud-based computing resources. However, many classification applications such as those in healthcare or defense introduce privacy concerns that prevent the collection of such data and the use of pre-existing large scale computing systems. Although many solutions to privacy preserving machine learning have previously been explored, the added computational complexity incurred with training on encrypted values inhibits these systems from executing in real-time. One of the most promising solutions that facilitates secure machine learning is secure multi-party computation (MPC), which relies on segmenting data across multiple devices such that the original data cannot be reconstructed without recombining each of the data segments. This thesis explores the efficacy of training CNNs on encrypted data using MPC techniques and utilizes several optimization techniques to lessen the computational and communication overheads incurred from doing so. The goals are to create a privacy-preserving CNN framework that achieves testing accuracy similar to a non-secure model while introducing the least amount of computational overhead. To this end, a multi-party encryption scheme was used to encrypt all floating point values used in training, and federated learning was incorporated to reduce the effects of the computational overhead by parallelizing the training of the network. The developed secure CNN was able to achieve validation accuracy within 1.1-2.8% of a baseline CNN on the MNIST dataset and 9.9-19.4% on the CIFAR-10 dataset. This decreased accuracy is caused by rounding errors incurred by performing multiple continuous arithmetic computations in the secure domain during training, however the accuracy results of the secure CNN indicate that training can be performed on encrypted values. The cost of performing training on encrypted values was found to range from between 8 - 21x more computation time in comparison to a non-secure baseline implementation due to the added computational complexity and communication overhead required to perform training on secure values. This additional training time, however, was shown to be able to be mitigated through the use of federated averaging by performing training on multiple devices in parallel