Biblio

Hardware-assisted isolation technology provide a Trusted Execution Environment (TEE) for the Trusted Computing Base (TCB) of a system. Since there is no standardization for such systems, many technologies using different approaches have been implemented over time. Before selecting or implementing a TEE, it is essential to understand the security architecture, features and analyze the technologies with respect to the new security vulnerabilities (i.e. Micro-architectural class of vulnerabilities). These technologies can be divided into two main types: 1) Isolation by software virtualization and 2) Isolation by hardware. In this paper, we discuss technology implementation of each type i.e. Intel SGX and ARM TrustZone for type-1; Intel ME and AMD Secure Processor for type-2. We also cover the vulnerability analysis against each technology with respect to the latest discovered attacks. This would enable a user to precisely appreciate the security capabilities of each technology.

Vulnerabilities in privileged software layers have been exploited with severe consequences. Recently, Trusted Execution Environments (TEEs) based technologies have emerged as a promising approach since they claim strong confidentiality and integrity guarantees regardless of the trustworthiness of the underlying system software. In this paper, we consider one of the most prominent TEE technologies, referred to as Intel Software Guard Extensions (SGX). Despite many formal approaches, there is still a lack of formal proof of some critical processes of Intel SGX, such as remote attestation. To fill this gap, we propose a fully automated, rigorous, and sound formal approach to specify and verify the Enhanced Privacy ID (EPID)-based remote attestation in Intel SGX under the assumption that there are no side-channel attacks and no vulnerabilities inside the enclave. The evaluation indicates that the confidentiality of attestation keys is preserved against a Dolev-Yao adversary in this technology. We also present a few of the many inconsistencies found in the existing literature on Intel SGX attestation during formal specification.

File sharing applications using cloud storage are increasingly popular for personal and business use. Due to data protection concerns, end-to-end encryption is often a desired feature of these applications. Many attempts at designing cryptographic solutions fail to be adopted due to missing relevant features. We present SeGShare, a new architecture for end-to-end encrypted, group-based file sharing using trusted execution environments (TEE), e.g., Intel SGX. SeGShare is the first solution to protect the confidentiality and integrity of all data and management files; enforce immediate permission and membership revocations; support deduplication; and mitigate rollback attacks. Next to authentication, authorization and file system management, our implementation features an optimized TLS layer that enables high throughput and low latency. The encryption overhead of our implementation is extremely small in computation and storage resources. Our enclave code comprises less than 8500 lines of code enabling efficient mitigation of common pitfalls in deploying code to TEEs.

In recent years, with the progress of data analysis methods utilizing artificial intelligence (AI) technology, concepts of smart cities collecting data from IoT devices and creating values by analyzing it have been proposed. However, making sure that the data is not tampered with is of the utmost importance. One way to do this is to utilize blockchain technology to record and trace the history of the data. Park and Kim proposed ensuring the trustworthiness of the data by utilizing an IoT device with a trusted execution environment (TEE). Also, Guan et al. proposed authenticating an IoT device and mediating data using a TEE. For the authentication, they use the physically unclonable function of the IoT device. Usually, IoT devices suffer from the lack of resources necessary for creating transactions for the blockchain ledger. In this paper, we present a secure protocol in which a TEE acts as a proxy to the IoT devices and creates the necessary transactions for the blockchain. We use an authenticated encryption method on the data transmission between the IoT device and TEE to authenticate the device and ensure the integrity and confidentiality of the data generated by the IoT devices.

The growing adoption of IoT devices in our daily life engendered a need for secure systems to safely store and analyze sensitive data as well as the real-time data processing system to be as fast as possible. The cloud services used to store and process sensitive data are often come out to be vulnerable to outside threats. Furthermore, to analyze streaming IoT data swiftly, they are in need of a fast and efficient system. The Paper will envision the aspects of complexity dealing with real time data from various devices in parallel, building solution to ingest data from different IOT devices, forming a secure platform to process data in a short time, and using various techniques of IOT edge computing to provide meaningful intuitive results to users. The paper envisions two modules of building a real time data analytics system. In the first module, we propose to maintain confidentiality and integrity of IoT data, which is of paramount importance, and manage large-scale data analytics with real-time data collection from various IoT devices in parallel. We envision a framework to preserve data privacy utilizing Trusted Execution Environment (TEE) such as Intel SGX, end-to-end data encryption mechanism, and strong access control policies. Moreover, we design a generic framework to simplify the process of collecting and storing heterogeneous data coming from diverse IoT devices. In the second module, we envision a drone-based data processing system in real-time using edge computing and on-device computing. As, we know the use of drones is growing rapidly across many application domains including real-time monitoring, remote sensing, search and rescue, delivery of goods, security and surveillance, civil infrastructure inspection etc. This paper demonstrates the potential drone applications and their challenges discussing current research trends and provide future insights for potential use cases using edge and on-device computing.

Emerging hardware architectures for Deep Neural Networks (DNNs) are being commercialized and considered as the hardware- level Intellectual Property (IP) of the device providers. However, these intelligent devices might be abused and such vulnerability has not been identified. The unregulated usage of intelligent platforms and the lack of hardware-bounded IP protection impair the commercial advantage of the device provider and prohibit reliable technology transfer. Our goal is to design a systematic methodology that provides hardware-level IP protection and usage control for DNN applications on various platforms. To address the IP concern, we present DeepAttest, the first on-device DNN attestation method that certifies the legitimacy of the DNN program mapped to the device. DeepAttest works by designing a device-specific fingerprint which is encoded in the weights of the DNN deployed on the target platform. The embedded fingerprint (FP) is later extracted with the support of the Trusted Execution Environment (TEE). The existence of the pre-defined FP is used as the attestation criterion to determine whether the queried DNN is authenticated. Our attestation framework ensures that only authorized DNN programs yield the matching FP and are allowed for inference on the target device. DeepAttest provisions the device provider with a practical solution to limit the application usage of her manufactured hardware and prevents unauthorized or tampered DNNs from execution. We take an Algorithm/Software/Hardware co-design approach to optimize DeepAttest's overhead in terms of latency and energy consumption. To facilitate the deployment, we provide a high-level API of DeepAttest that can be seamlessly integrated into existing deep learning frameworks and TEEs for hardware-level IP protection and usage control. Extensive experiments corroborate the fidelity, reliability, security, and efficiency of DeepAttest on various DNN benchmarks and TEE-supported platforms.

Trusted Execution Environments (TEEs) provide hardware support to isolate the execution of sensitive operations on mobile phones for improved security. However, they are not always available to use for application developers. To provide a consistent user experience to those who have and do not have a TEE-enabled device, we could get help from Open-TEE, an open-source GlobalPlatform (GP)-compliant software TEE emulator. However, Open-TEE does not offer any of the security properties hardware TEEs have. In this paper, we propose WhiteBox-TEE which integrates white-box cryptography with Open-TEE to provide better security while still remaining complaint with GP TEE specifications. We discuss the architecture, provisioning mechanism, implementation highlights, security properties and performance issues of WhiteBox-TEE and propose possible revisions to TEE specifications to have better use of white-box cryptography in software-only TEEs.

Many blockchain transactions require blockchain-external data sources to provide data. Oracle systems have been proposed as a link between blockchains and blockchain-external resources. However, these Oracle systems vary greatly in assumptions and applicability and each system addresses the challenge of data on-chaining partly. We argue that Data On-chaining must be done in a trustworthy manner and, as a first contribution, define a set of key requirements for Trustworthy Data On-chaining. Further, we provide an in-depth assessment and comparison of state-of-the-art Oracle systems with regards to these requirements. This differentiation pinpoints the need for a uniform understanding of and directions for future research on Trustworthy Data On-chaining.

On ARM processors with TrustZone security extension, asynchronous introspection mechanisms have been developed in the secure world to detect security policy violations in the normal world. These mechanisms provide security protection via passively checking the normal world snapshot. However, since previous secure world checking solutions require to suspend the entire rich OS, asynchronous introspection has not been widely adopted in the real world. Given a multi-core ARM system that can execute the two worlds simultaneously on different cores, secure world introspection can check the rich OS without suspension. However, we identify a new normal-world evasion attack that can defeat the asynchronous introspection by removing the attacking traces in parallel from one core when the security checking is performing on another core. We perform a systematic study on this attack and present its efficiency against existing asynchronous introspection mechanisms. As the countermeasure, we propose a secure and trustworthy asynchronous introspection mechanism called SATIN, which can efficiently detect the evasion attacks by increasing the attackers' evasion time cost and decreasing the defender's execution time under a safe limit. We implement a prototype on an ARM development board and the experimental results show that SATIN can effectively prevent evasion attacks on multi-core systems with a minor system overhead.

Notions like security, trust, and privacy are crucial in the digital environment and in the future, with the advent of technologies like the Internet of Things (IoT) and Cyber-Physical Systems (CPS), their importance is only going to increase. Trust has different definitions, some situations rely on real-world relationships between entities while others depend on robust technologies to gain trust after deployment. In this paper we focus on these robust technologies, their evolution in past decades and their scope in the near future. The evolution of robust trust technologies has involved diverse approaches, as a consequence trust is defined, understood and ascertained differently across heterogeneous domains and technologies. In this paper we look at digital trust technologies from the point of view of security and examine how they are making secure computing an attainable reality. The paper also revisits and analyses the Trusted Platform Module (TPM), Secure Elements (SE), Hypervisors and Virtualisation, Intel TXT, Trusted Execution Environments (TEE) like GlobalPlatform TEE, Intel SGX, along with Host Card Emulation, and Encrypted Execution Environment (E3). In our analysis we focus on these technologies and their application to the emerging domains of the IoT and CPS.

In the universal Android system, each application runs in its own sandbox, and the permission mechanism is used to enforce access control to the system APIs and applications. However, permission leak could happen when an application without certain permission illegally gain access to protected resources through other privileged applications. In order to address permission leak in a trusted execution environment, this paper designs security architecture which contains sandbox module, middleware module, usage and access control module, and proposes an effective usage and access control scheme that can prevent permission leak in a trusted execution environment. Security architecture based on the scheme has been implemented on an ARM-Android platform, and the evaluation of the proposed scheme demonstrates its effectiveness in mitigating permission leak vulnerabilities.

Near Field Communication (NFC)-based mobile phone services offer a lifeline to the under-appreciated multiapplication smart card initiative. The initiative could effectively replace heavy wallets full of smart cards for mundane tasks. However, the issue of the deployment model still lingers on. Possible approaches include, but are not restricted to, the User Centric Smart card Ownership Model (UCOM), GlobalPlatform Consumer Centric Model, and Trusted Service Manager (TSM). In addition, multiapplication smart card architecture can be a GlobalPlatform Trusted Execution Environment (TEE) and/or User Centric Tamper-Resistant Device (UCTD), which provide cross-device security and privacy preservation platforms to their users. In the multiapplication smart card environment, there might not be a prior off-card trusted relationship between a smart card and an application provider. Therefore, as a possible solution to overcome the absence of prior trusted relationships, this paper proposes the concept of Trusted Platform Module (TPM) for smart cards (embedded devices) that can act as a point of reference for establishing the necessary trust between the device and an application provider, and among applications.