SGX-Bundler: speeding up enclave transitions for IO-intensive applications

Process-based confidential computing enclaves such as Intel SGX can be used to protect the confidentiality and integrity of workloads, without the overhead of virtualisation. However, they introduce a notable performance overhead, especially when it comes to transitions in and out of the enclave context. Such overhead makes the use of enclaves impractical for running IO-intensive applications, such as network packet processing or biological sequence analysis. We build on earlier approaches to improve the IO performance of work-loads in Intel SGX enclaves and propose the SGX-Bundler library, which helps reduce the cost of both individual single enclave transitions well as of the total number of enclave transitions in trusted applications running in Intel SGX enclaves. We describe the implementation of the SGX-Bundler library, evaluate its performance and demonstrate its practicality using the case study of Open vSwitch, a widely used software switch implementation

Strongly Secure and Efficient Data Shuffle On Hardware Enclaves

Mitigating memory-access attacks on the Intel SGX architecture is an important and open research problem. A natural notion of the mitigation is cache-miss obliviousness which requires the cache-misses emitted during an enclave execution are oblivious to sensitive data. This work realizes the cache-miss obliviousness for the computation of data shuffling. The proposed approach is to software-engineer the oblivious algorithm of Melbourne shuffle on the Intel SGX/TSX architecture, where the Transaction Synchronization eXtension (TSX) is (ab)used to detect the occurrence of cache misses. In the system building, we propose software techniques to prefetch memory data prior to the TSX transaction to defend the physical bus-tapping attacks. Our evaluation based on real implementation shows that our system achieves superior performance and lower transaction abort rate than the related work in the existing literature.Comment: Systex'1

Faster enclave transitions for IO-intensive network applications

Process-based confidential computing enclaves such as Intel SGX have been proposed for protecting the confidentiality and integrity of network applications, without the overhead of virtualization. However, these solutions introduce other types of overhead, particularly the cost transitioning in and out of an enclave context. This makes the use of enclaves impractical for running IO-intensive applications, such as network packet processing. We build on ear- lier approaches to improve the IO performance of workloads in Intel SGX enclaves and propose the HotCall-Bundler library that helps reduce the cost of individual single enclave transitions and the total number of enclave transitions in trusted applications running in Intel SGX enclaves. We describe the implementation of the HotCall-Bundler library, evaluate its performance and demonstrate its practicality using the case study of Open vSwitch, a widely used software switch implementation

Secure Cloud Storage with Client-Side Encryption Using a Trusted Execution Environment

With the evolution of computer systems, the amount of sensitive data to be stored as well as the number of threats on these data grow up, making the data confidentiality increasingly important to computer users. Currently, with devices always connected to the Internet, the use of cloud data storage services has become practical and common, allowing quick access to such data wherever the user is. Such practicality brings with it a concern, precisely the confidentiality of the data which is delivered to third parties for storage. In the home environment, disk encryption tools have gained special attention from users, being used on personal computers and also having native options in some smartphone operating systems. The present work uses the data sealing, feature provided by the Intel Software Guard Extensions (Intel SGX) technology, for file encryption. A virtual file system is created in which applications can store their data, keeping the security guarantees provided by the Intel SGX technology, before send the data to a storage provider. This way, even if the storage provider is compromised, the data are safe. To validate the proposal, the Cryptomator software, which is a free client-side encryption tool for cloud files, was integrated with an Intel SGX application (enclave) for data sealing. The results demonstrate that the solution is feasible, in terms of performance and security, and can be expanded and refined for practical use and integration with cloud synchronization services

Mitigating Branch-Shadowing Attacks on Intel SGX using Control Flow Randomization

Intel Software Guard Extensions (SGX) is a promising hardware-based technology for protecting sensitive computations from potentially compromised system software. However, recent research has shown that SGX is vulnerable to branch-shadowing -- a side channel attack that leaks the fine-grained (branch granularity) control flow of an enclave (SGX protected code), potentially revealing sensitive data to the attacker. The previously-proposed defense mechanism, called Zigzagger, attempted to hide the control flow, but has been shown to be ineffective if the attacker can single-step through the enclave using the recent SGX-Step framework. Taking into account these stronger attacker capabilities, we propose a new defense against branch-shadowing, based on control flow randomization. Our scheme is inspired by Zigzagger, but provides quantifiable security guarantees with respect to a tunable security parameter. Specifically, we eliminate conditional branches and hide the targets of unconditional branches using a combination of compile-time modifications and run-time code randomization. We evaluated the performance of our approach by measuring the run-time overhead of ten benchmark programs of SGX-Nbench in SGX environment

Stress-SGX: Load and Stress your Enclaves for Fun and Profit

The latest generation of Intel processors supports Software Guard Extensions (SGX), a set of instructions that implements a Trusted Execution Environment (TEE) right inside the CPU, by means of so-called enclaves. This paper presents Stress-SGX, an easy-to-use stress-test tool to evaluate the performance of SGX-enabled nodes. We build on top of the popular Stress-NG tool, while only keeping the workload injectors (stressors) that are meaningful in the SGX context. We report on several insights and lessons learned about porting legacy code to run inside an SGX enclave, as well as the limitations introduced by this process. Finally, we use Stress-SGX to conduct a study comparing the performance of different SGX-enabled machines.Comment: European Commission Project: LEGaTO - Low Energy Toolset for Heterogeneous Computing (EC-H2020-780681

SGX Switchless Calls Made Configless

Intel's software guard extensions (SGX) provide hardware enclaves to guarantee confidentiality and integrity for sensitive code and data. However, systems leveraging such security mechanisms must often pay high performance overheads. A major source of this overhead is SGX enclave transitions which induce expensive cross-enclave context switches. The Intel SGX SDK mitigates this with a switchless call mechanism for transitionless cross-enclave calls using worker threads. Intel's SGX switchless call implementation improves performance but provides limited flexibility: developers need to statically fix the system configuration at build time, which is error-prone and misconfigurations lead to performance degradations and waste of CPU resources. ZC-SWITCHLESS is a configless and efficient technique to drive the execution of SGX switchless calls. Its dynamic approach optimises the total switchless worker threads at runtime to minimise CPU waste. The experimental evaluation shows that ZC-SWITCHLESS obviates the performance penalty of misconfigured switchless systems while minimising CPU waste.Comment: 10 pages, 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN

Systems Support for Trusted Execution Environments

Cloud computing has become a default choice for data processing by both large corporations and individuals due to its economy of scale and ease of system management. However, the question of trust and trustoworthy computing inside the Cloud environments has been long neglected in practice and further exacerbated by the proliferation of AI and its use for processing of sensitive user data. Attempts to implement the mechanisms for trustworthy computing in the cloud have previously remained theoretical due to lack of hardware primitives in the commodity CPUs, while a combination of Secure Boot, TPMs, and virtualization has seen only limited adoption. The situation has changed in 2016, when Intel introduced the Software Guard Extensions (SGX) and its enclaves to the x86 ISA CPUs: for the first time, it became possible to build trustworthy applications relying on a commonly available technology. However, Intel SGX posed challenges to the practitioners who discovered the limitations of this technology, from the limited support of legacy applications and integration of SGX enclaves into the existing system, to the performance bottlenecks on communication, startup, and memory utilization. In this thesis, our goal is enable trustworthy computing in the cloud by relying on the imperfect SGX promitives. To this end, we develop and evaluate solutions to issues stemming from limited systems support of Intel SGX: we investigate the mechanisms for runtime support of POSIX applications with SCONE, an efficient SGX runtime library developed with performance limitations of SGX in mind. We further develop this topic with FFQ, which is a concurrent queue for SCONE's asynchronous system call interface. ShieldBox is our study of interplay of kernel bypass and trusted execution technologies for NFV, which also tackles the problem of low-latency clocks inside enclave. The two last systems, Clemmys and T-Lease are built on a more recent SGXv2 ISA extension. In Clemmys, SGXv2 allows us to significantly reduce the startup time of SGX-enabled functions inside a Function-as-a-Service platform. Finally, in T-Lease we solve the problem of trusted time by introducing a trusted lease primitive for distributed systems. We perform evaluation of all of these systems and prove that they can be practically utilized in existing systems with minimal overhead, and can be combined with both legacy systems and other SGX-based solutions. In the course of the thesis, we enable trusted computing for individual applications, high-performance network functions, and distributed computing framework, making a <vision of trusted cloud computing a reality

SGXTuner: Performance Enhancement of Intel SGX Applications via Stochastic Optimization

Intel SGX has started to be widely adopted. Cloud providers (Microsoft Azure, IBM Cloud, Alibaba Cloud) are offering new solutions, implementing data-in-use protection via SGX. A major challenge faced by both academia and industry is providing transparent SGX support to legacy applications. The approach with the highest consensus is linking the target software with SGX-extended libc libraries. Unfortunately, the increased security entails a dramatic performance penalty, which is mainly due to the intrinsic overhead of context switches, and the limited size of protected memory. Performance optimization is non-trivial since it depends on key parameters whose manual tuning is a very long process. We present the architecture of an automated tool, called SGXTuner, which is able to find the best setting of SGX-extended libc library parameters, by iteratively adjusting such parameters based on continuous monitoring of performance data. The tool is to a large extent algorithm agnostic. We decided to base the current implementation on a particular type of stochastic optimization algorithm, specifically Simulated Annealing. A massive experimental campaign was conducted on a relevant case study. Three client-server applications Memcached, Redis, and Apache were compiled with SCONE's sgx-musl and tuned for best performance. Results demonstrate the effectiveness of SGXTuner