Trusted platforms have been proposed as a promising approach to enhance the security of general-purpose computing systems. However, for many resource-constrained embedded systems, the size and cost overheads of a separate Trusted Platform Module (TPM) chip are not acceptable. One alternative is to use a software-based TPM, which implements TPM functions using software that executes in a protected execution domain on the embedded processor itself. However, since many embedded systems have limited processing capabilities and are battery-powered, it is also important to ensure that the computational and energy requirements for SW-TPMs are acceptable. In this article, we perform an evaluation of the energy and execution time overheads for a SW-TPM implementation on a handheld appliance (Sharp Zaurus PDA). We characterize the execution time and energy required by each TPM command through actual measurements on the target platform. We observe that for most commands, overheads are primarily due to the use of 2,048-bit RSA operations that are performed within the SW-TPM. In order to alleviate SW-TPM overheads, we evaluate the use of Elliptic Curve Cryptography (ECC) as a replacement for the RSA algorithm specified in the Trusted Computing Group (TCG) standards. In addition, we also evaluate the overheads of using the SW-TPM in the context of various end applications, including trusted boot of the Linux operating system (OS), a secure VoIP client, and a secure Web browser. Furthermore, we analyze the computational workload involved in running SW-TPM commands using ECC. We then present a suite of hardware and software enhancements to accelerate these commandsgeneric custom instructions and exploitation of parallel processing capabilities in multiprocessor systems-on-chip (SoCs). We report results of evaluating the proposed architectures on a commercial embedded processor (Xtensa from Tensilica). Through uniprocessor and multiprocessor optimizations, we could achieve speed-ups of up to 5.71X for individual TPM commands.
All Science Journal Classification (ASJC) codes
- Hardware and Architecture
- Custom instructions
- Embedded systems
- Multiprocessor systems