Secure and Efficient Software Masking on Superscalar Pipelined Processors

Physical side-channel attacks like power analysis pose a serious threat to cryptographic devices in real-world applications. Consequently, devices implement algorithmic countermeasures like masking. In the past, works on the design and verification of masked software implementations have mostly focused on simple microprocessors that find usage on smart cards. However, many other applications such as in the automotive industry require side-channel protected cryptographic computations on much more powerful CPUs. In such situations, the security loss due to complex architectural side-effects, the corresponding performance degradation, as well as discussions of suitable probing models and verification techniques are still vastly unexplored research questions. We answer these questions and perform a comprehensive analysis of more complex processor architectures in the context of masking-related side effects. First, we analyze the RISC-V SweRV core — featuring a 9-stage pipeline, two execution units, and load/store buffers — and point out a significant gap between security in a simple software probing model and practical security on such CPUs. More concretely, we show that architectural side effects of complex CPU architectures can significantly reduce the protection order of masked software, both via formal analysis in the hardware probing model, as well as empirically via gate-level timing simulations. We then discuss the options of fixing these problems in hardware or leaving them as constraints to software. Based on these software constraints, we formulate general rules for the design of masked software on more complex CPUs. Finally, we compare several implementation strategies for masking schemes and present in a case study that designing secure masked software for complex CPUs is still possible with overhead as low as 13%

Lock-free Concurrent Data Structures

Concurrent data structures are the data sharing side of parallel programming. Data structures give the means to the program to store data, but also provide operations to the program to access and manipulate these data. These operations are implemented through algorithms that have to be efficient. In the sequential setting, data structures are crucially important for the performance of the respective computation. In the parallel programming setting, their importance becomes more crucial because of the increased use of data and resource sharing for utilizing parallelism. The first and main goal of this chapter is to provide a sufficient background and intuition to help the interested reader to navigate in the complex research area of lock-free data structures. The second goal is to offer the programmer familiarity to the subject that will allow her to use truly concurrent methods.Comment: To appear in "Programming Multi-core and Many-core Computing Systems", eds. S. Pllana and F. Xhafa, Wiley Series on Parallel and Distributed Computin

Secure Context Switching of Masked Software Implementations

Cryptographic software running on embedded devices requires protection against physical side-channel attacks such as power analysis. Masking is a widely deployed countermeasure against these attacksand is directly implemented on algorithmic level. Many works study the security of masked cryptographic software on CPUs, pointing out potential problems on algorithmic/microarchitecture-level, as well as corresponding solutions, and even show masked software can be implemented efficiently and with strong (formal) security guarantees. However, these works also make the implicit assumption that software is executed directly on the CPU without any abstraction layers in-between, i.e., they focus exclusively on the bare-metal case. Many practical applications, including IoT and automotive/industrial environments, require multitasking embedded OSs on which masked software runs as one out of many concurrent tasks. For such applications, the potential impact of events like context switches on the secure execution of masked software has not been studied so far at all. In this paper, we provide the first security analysis of masked cryptographic software spanning all three layers (SW, OS, CPU). First, we apply a formal verification approach to identify leaks within the execution of masked software that are caused by the embedded OS itself, rather than on algorithmic or microarchitecture level. After showing that these leaks are primarily caused by context switching, we propose several different strategies to harden a context switching routine against such leakage, ultimately allowing masked software from previous works to remain secure when being executed on embedded OSs. Finally, we present a case study focusing on FreeRTOS, a popular embedded OS for embedded devices, running on a RISC-V core, allowing us to evaluate the practicality and ease of integration of each strategy

SoK: Design Tools for Side-Channel-Aware Implementations

Side-channel attacks that leak sensitive information through a computing device's interaction with its physical environment have proven to be a severe threat to devices' security, particularly when adversaries have unfettered physical access to the device. Traditional approaches for leakage detection measure the physical properties of the device. Hence, they cannot be used during the design process and fail to provide root cause analysis. An alternative approach that is gaining traction is to automate leakage detection by modeling the device. The demand to understand the scope, benefits, and limitations of the proposed tools intensifies with the increase in the number of proposals. In this SoK, we classify approaches to automated leakage detection based on the model's source of truth. We classify the existing tools on two main parameters: whether the model includes measurements from a concrete device and the abstraction level of the device specification used for constructing the model. We survey the proposed tools to determine the current knowledge level across the domain and identify open problems. In particular, we highlight the absence of evaluation methodologies and metrics that would compare proposals' effectiveness from across the domain. We believe that our results help practitioners who want to use automated leakage detection and researchers interested in advancing the knowledge and improving automated leakage detection