ShieldStore

Trusted execution as offered by Intel Software Guard Extensions (SGX) enables confidentiality and integrity for cloud-hosted services. While in principle all kinds of workloads can be secured using trusted execution, key-value stores have gained special attention as these services are an essential building block of most complex cloud deployments. So far the main design challenge has been to address current performance limitations of SGX. In addition, we identify the integration of SGX-guarded workloads with recent network technology, especially Remote Direct Memory Access (RDMA) as an upcoming requirement. RDMA allows fast direct access to remote memory at high bandwidth. However, SGX protected memory cannot be directly accessed over the network. Furthermore, due to the more powerful network, secured services might likely be CPU-bound as a result of the necessary cryptographic operations.

Intel Software Guard Extensions (SGX) is a promising hardware-based technology for protecting sensitive computation 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 using ten benchmarks from SGX-Nbench. Although we considered the worst-case scenario (whole program instrumentation), our results show that, on average, our approach results in less than 18% performance loss and less than 1.2 times code size increase.

Intel Software Guard Extensions (SGX) is a recent technology from Intel that makes it possible to execute security-critical parts of an application in a so-called SGX enclave, an isolated area of the system that is shielded from all other software (including the OS and/or hypervisor). SGX was designed with the objective of making it relatively straightforward to take a single module of an existing C application, and put that module in an enclave. The SGX SDK includes tooling to semi-automatically generate wrappers for an enclaved C module. The wrapped enclave can then easily be linked to the legacy application that uses the module.

As a decentralized, public, and digital ledger technology in Peer-to-Peer network, blockchain has received much attention from various fields, including finance, healthcare, supply chain, etc. However, some challenges (e.g., scalability, privacy, and security issues) severely affects the wide adoption of blockchain technology. Recently, Intel software guard extensions (SGX), as new trusted computing technologies, have provided a new solution to the above challenges in the blockchain area. Although many studies have focused on using SGX technology to enhance their schemes in the blockchain areas, no comprehensive survey has systematically analyzed and delineated these studies. This article is the first to systematically discuss the application status of SGX in the blockchain area. In this article, we study the scheme designs, advantages, and disadvantages of the existing works using a six-layer hierarchical structure of the blockchain. We also summarize the functions of SGX and formally analyze the advantages and disadvantages of SGX. Finally, we review the remaining challenges and present a list of possible directions for future research.

Intel's Software Guard Extensions (SGX) are currently the most wide-spread commodity trusted execution environment, which provides integrity and confidentiality of sensitive code and data. Thereby, it offers protection even against privileged attackers and various forms of physical attacks. As a technology that only became available in late 2015, it has received massive interest and undergone a rapid evolution. Despite first ad-hoc attempts, there is so far no standardised approach to benchmark the SGX hardware, its associated environment, and techniques that were designed to harden SGX-based applications. In this paper, we present SGXoMeter, an open and modular framework designed to benchmark different SGX-aware CPUs, mcode revisions, SDK versions and extensions to mitigate side-channel attacks. SGXoMeter provides a set of practical SGX test case scenarios and eases the development of custom benchmarks. Furthermore, we compare it to sgx-nbench, the only other SGX application benchmark tool we are aware of, and evaluate their differences. Through our benchmark results, we identified a performance overhead of up to a10 times induced between two different SGX-SDK versions for certain workload scenarios.

SGX-LEGO: Fine-grained SGX controlled-channel attack and its countermeasure

This paper presents a comprehensive survey on the development of Intel SGX (software guard extensions) processors and its applications. With the advent of SGX in 2013 and its subsequent development, the corresponding research works are also increasing rapidly. In order to get a more comprehensive literature review related to SGX, we have made a systematic analysis of the related papers in this area. We first search through five large-scale paper retrieval libraries by keywords (i.e., ACM Digital Library, IEEE/IET Electronic Library, SpringerLink, Web of Science, and Elsevier Science Direct). We read and analyze a total of 128 SGX-related papers. The first round of extensive study is conducted to classify them. The second round of intensive study is carried out to complete a comprehensive analysis of the paper from various aspects. We start with the working environment of SGX and make a conclusive summary of trusted execution environment (TEE). We then focus on the applications of SGX. We also review and study multifarious attack methods to SGX framework and some recent security improvements made on SGX. Finally, we summarize the advantages and disadvantages of SGX with some future research opportunities. We hope this review could help the existing and future research works on SGX and its application for both developers and users.

Trusted Execution Environment (TEE) is designed to deliver a safe execution environment for software systems. Intel Software Guard Extensions (SGX) provides isolated memory regions (i.e., SGX enclaves) to protect code and data from adversaries in the untrusted world. While existing research has proposed techniques to execute entire executable fles inside enclave instances by providing rich sets of OS facilities, one notable limitation of these techniques is the unavoidably large size of Trusted Computing Base (TCB), which can potentially break the principle of least privilege. In this work, we describe techniques that provide practical and efcient protection of security sensitive code components in legacy binary code. Our technique dissects input binaries into multiple components which are further built into SGX enclave instances. We also leverage deliberately-designed binary editing techniques to retroft the input binary code and preserve the original program semantics. Our tentative evaluations on hardening AES encryption and decryption procedures demonstrate the practicability and efciency of the proposed technique. © 2017 Association for Computing Machinery.

Threats to data processing in the cloud computing environment are increasing with emerging cloud applications. Intel Software Guard Extensions (SGX) implements a Trusted Execution Environment (TEE) through hardware-based isolation, which can enhance the security of cloud applications by providing a separate secure space that does not reveal its interior. A trusted memory area isolated by SGX, so-called enclave, protects the confidential code and data from any other software, including highly privileged system software. However, the adversary also benefits from isolation by inserting malicious code into the enclave where the system cannot detect it. Existing studies suggest various measures to deal with enclave malware, but these are based on an underlying assumption that the system must be trusted, which does not apply to the semi-honest cloud platform. To this end, we propose a novel method, named Interclave, that protects the system from enclave malware in the cloud computing environment without trusting any component other than the intermediate enclave. Interclave forces every ECALL and OCALL of a suspicious enclave to be executed through an intermediate enclave. This prevents the malicious behavior of enclave malware such as code-reuse attacks that modify the stack or register values associated with the program's execution address. Interclave stores and restores all stack and register values before and after the execution of the suspicious enclave, ensuring the integrity of these values. Our implementation is done with slight code modifications which occupy only a little storage capacity without complicated hardware changes.

Due to the absence of in-enclave isolation, today's trusted execution environment (TEE), specifically Intel's Software Guard Extensions (SGX), does not have the capability to securely run different users' tasks within a single enclave, which is required for supporting real-world services, such as an in-enclave machine learning model that classifies the data from various sources, or a microservice (e.g., data search) that performs a very small task (within sub-seconds) for a user and therefore cannot afford the resources and the delay for creating a separate enclave for each user. To address this challenge, we developed Liveries, a technique that enables lightweight, verifiable in-enclave user isolation for protecting time-sharing services. Our approach restricts an in-enclave thread's privilege when configuring an enclave, and further performs integrity check and sanitization on critical enclave data upon user switches. For this purpose, we developed a novel technique that ensures the protection of sensitive user data (e.g., session keys) even in the presence of the adversary who may have compromised the enclave. Our study shows that the new technique is lightweight (1% overhead) and verifiable (about 3200 lines of code), making a step towards assured protection of real-world in-enclave services.

Cloud-based technologies play an increasing role in software engineering because of their scalability, availability, and cost efficiency. However, due to privacy issues, developers and organizations still hesitate to host applications that handle sensitive data on servers of external cloud providers. Modern hardware extensions, such as Intel's Software Guard Extensions (SGX), are an attempt to provide confidentiality and integrity for applications running on external hardware. Still, enabling SGX in cloud systems poses new challenges considering scalability and flexibility. In this paper, we propose an approach to address these issues by employing concepts from the domain of Dynamic Software Product Lines (DSPLs). We aim to enable applications running on SGX-based cloud systems to be securely reconfigurable and extendable during runtime. In particular, we describe properties that such an approach should fulfill and discuss corresponding challenges.

Users of modern data-processing services such as tax preparation or genomic screening are forced to trust them with data that the users wish to keep secret. Ryoan protects secret data while it is processed by services that the data owner does not trust. Accomplishing this goal in a distributed setting is difficult because the user has no control over the service providers or the computational platform. Confining code to prevent it from leaking secrets is notoriously difficult, but Ryoan benefits from new hardware and a request-oriented data model.Ryoan provides a distributed sandbox, leveraging hardware enclaves (e.g., Intel's software guard extensions (SGX) [15]) to protect sandbox instances from potentially malicious computing platforms. The protected sandbox instances confine untrusted data-processing modules to prevent leakage of the user's input data. Ryoan is designed for a request-oriented data model, where confined modules only process input once and do not persist state about the input. We present the design and prototype implementation of Ryoan and evaluate it on a series of challenging problems including email filtering, heath analysis, image processing and machine translation.

The recent increase of devices connected to the Internet and the introduction of new paradigms; Internet of Things (IoT) and Cloud computing, exposes the Internet to sever security risks, especially malicious alteration of the application software code or utilizing malfunction codes to attack the system. Such an attack can change the behavior and the outcome of the system. This paper suggests a system that identifies unsecured software codes and fixes them to ensure risk safe codes. To execute the proposed system in a secured development and deployment environment, protected from disclosure or modification of the code, we propose using the Intel Software Guard Extensions (SGX) environment. Using a list of predefined rules, the system looks for suspicious codes and replaces them with safe codes associated with the rule.

An accountable and confidential data crowdsourcing framework comprised of the trusted execution environment (TEE), blockchain, and a distributed file storage system was proposed in this paper. To begin, participants may register using their true identity message, and only the guardians (or regulators) can securely review their registration information. Then, the requester submits and the executor republishes the crowdsourcing task con-fidentially. Following that, workers collect and share data via their smart devices' Trust Zone, and executors assemble, validate data in their Intel Software Guard Extensions (SGX). Furthermore, the data exchanged between participants is encrypted, and only au-thorized users can view them. According to the analysis and exten-sive experiments, our proposed system outperforms others especially in terms of latency, throughput, and task success rate.

Hardware enclaves assist in constructing a trusted execution environment (TEE) to store private code and data and thus become an appealing solution to enhance applications’ security. Nevertheless, state-of-the-art enclave implementations like Intel Software Guard Extensions (SGX) have severe performance issues and hinder the deployment of more complicated applications, especially those written in high-level languages like Java. To reduce the performance overhead, prior work has partitioned applications or rebuilt lightweight language runtimes, but they either require manual labor from developers or fail to provide full-fledged support for existing applications. This work instead provides <i>SAJ</i> , a runtime built upon a full-fledged Java virtual machine (JVM) and thus requires no modifications to applications. <i>SAJ</i> first analyzes the performance of vanilla JVMs running in enclaves and finds that the memory management overhead and boot phase are culprits for performance slowdown. For memory management, <i>SAJ</i> introduces SGX-aware heap layout and garbage collector, which reduces both GC and application execution time. As for the boot phase, <i>SAJ</i> introduces an address-conscious launching mechanism to improve the boot performance. The evaluation under representative Java applications shows that <i>SAJ</i> can reduce the overall GC pause time, application time, and boot time by 2.93 <inline-formula><tex-math notation="LaTeX">$\boldsymbol{\times}$</tex-math></inline-formula> , 2.58 <inline-formula><tex-math notation="LaTeX">$\boldsymbol{\times}$</tex-math></inline-formula> , and 2.73 <inline-formula><tex-math notation="LaTeX">$\boldsymbol{\times}$</tex-math></inline-formula> on average, respectively.