Performing deep learning on end-user devices provides fast offline inference results and can help protect the user's privacy. However, running models on untrusted client devices reveals model information which may be proprietary, i.e., the operating system or other applications on end-user devices may be manipulated to copy and redistribute this information, infringing on the model provider's intellectual property. We propose the use of ARM TrustZone, a hardware-based security feature present in most phones, to confidentially run a proprietary model on an untrusted end-user device. We explore the limitations and design challenges of using TrustZone and examine potential approaches for confidential deep learning within this environment. Of particular interest is providing robust protection of proprietary model information while minimizing total performance overhead.
As machine learning (ML) technologies and applications are rapidly changing many computing domains, security issues associated with ML are also emerging. In the domain of systems security, many endeavors have been made to ensure ML model and data confidentiality. ML computations are often inevitably performed in untrusted environments and entail complex multi-party security requirements. Hence, researchers have leveraged the Trusted Execution Environments (TEEs) to build confidential ML computation systems. We conduct a systematic and comprehensive survey by classifying attack vectors and miti…
With the ever-growing data and the need for developing powerful machine learning models, data owners increasingly depend on various untrusted platforms (e.g., public clouds, edges, and machine learning service providers) for scalable processing or collaborative learning. Thus, sensitive data and models are in danger of unauthorized access, misuse, and privacy compromises. A relatively new body of research confidentially trains machine learning models on protected data to address these concerns. In this survey, we summarize notable studies in this emerging area of research. With a unified frame…
Cloud computing allows shared computer and storage facilities to be used by a multitude of clients. While cloud management is centralized, the information resides in the cloud and information sharing can be implemented via off-the-shelf techniques for multiuser databases. Users, however, are very diffident for not having full control over their sensitive data. Untrusted database-as-a-server techniques are neither readily extendable to the cloud environment nor easily understandable by non-technical users. To solve this problem, we present an approach where agents share reserved data in a secur…
Using public cloud services for storing and sharing confidential data requires end users to cryptographically protect both the data and the access to the data. In some cases, the identity of end users needs to remain confidential against the cloud provider and fellow users accessing the data. As such, the underlying cryptographic access control mechanism needs to ensure the anonymity of both data producers and consumers. We introduce A-SKY, a cryptographic access control extension capable of providing confidentiality and anonymity guarantees, all while efficiently scaling to large organization…
We present Airtnt, a novel scheme that enables users with CPUs that support Trusted Execution Environments (TEEs) and remote attestation to rent out computing time on secure enclaves to untrusted users. Airtnt makes use of the attestation capabilities of TEEs and smart contracts on distributed ledgers to guarantee the fair exchange of the payment and the result of an execution. Airtnt makes use of off-chain payment channels to allow requesters to pay executing nodes for intermediate "snapshots" of the state of an execution. Effectively, this step-by-step "compute-payment" cycle realises untrus…
High-assurance security systems require strong isolation from the untrusted world to protect the security-sensitive or privacy-sensitive data they process. Existing regulations impose that such systems must execute in a trustworthy operating system (OS) to ensure they are not collocated with untrusted software that might negatively impact their availability or security. However, the existing techniques to attest to the OS integrity fall short due to the cuckoo attack. In this paper, we first show a novel defense mechanism against the cuckoo attack, and we formally prove it. Then, we implement …
EIFL: Efficiently Protecting Global Model Privacy and Integrity Against an Untrusted Server in Federated Learning
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；Department of Computer Science, Illinois Institute of Technology（伊利诺伊理工学院计算机科学系）
The enforcement of sensitive policies in untrusted environments is still an open challenge for policy-based systems. On the one hand, taking any appropriate security decision requires access to these policies. On the other hand, if such access is allowed in an untrusted environment then confidential information might be leaked by the policies. The key challenge is how to enforce sensitive policies and protect content in untrusted environments. In the context of untrusted environments, we mainly distinguish between outsourced and distributed environments. The most attractive paradigms concernin…
More Granular, Less Trust: Enforcing Intra-Process Isolation with Arm CCA in an Untrusted Management Environment
Huazhong University of Science and Technology（华中科技大学）；JinYinHu Laboratory（金银湖实验室）；George Mason University（乔治梅森大学）；Peking University（北京大学）；DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）
High-Performance Computing (HPC) in the public cloud democratizes the supercomputing power that most users cannot afford to purchase and maintain. Researchers have studied its viability, performance, and usability. However, HPC in the cloud has a unique feature -- users have to export data and computation to somewhat untrusted cloud platforms. Users will either fully trust cloud providers to protect from all kinds of attacks or keep sensitive assets in-house instead. With the recent deployment of the Trusted Execution Environment (TEE) in the cloud, confidential computing for HPC in the cloud …
virtCCA: Virtualized Arm Confidential Compute Architecture with TrustZone
Huawei Technologies（华为技术有限公司）；State Key Laboratory of Cyberspace Security Defense, Institute of Information Engineering, Chinese Academy of Sciences, and University of Chinese Academy of Sciences（中国科学院信息安全国家重点实验室，中国科学院信息工程研究所，中国科学院大学）；Shanghai Jiao Tong University（上海交通大学）；University of Science and Technology of China（中国科学技术大学）
The reliability and transmission distance are generally limited for the wireless communications due to the severe channel fading. As an effective way to resist the channel fading, cooperative relaying is usually adopted in wireless networks where neighbouring nodes act as relays to help the transmission between the source and the destination. Most research works simply regard these cooperative nodes trustworthy, which may be not practical in some cases especially when transmitting confidential information. In this paper, we consider the issue of untrusted relays in cooperative communications a…
The enormous amount of code required to design modern hardware implementations often leads to critical vulnerabilities being overlooked. Especially vulnerabilities that compromise the confidentiality of sensitive data, such as cryptographic keys, have a major impact on the trustworthiness of an entire system. Information flow analysis can elaborate whether information from sensitive signals flows towards outputs or untrusted components of the system. But most of these analytical strategies rely on the non-interference property, stating that the untrusted targets must not be influenced by the s…
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Recent advances in Deep Neural Networks (DNN) and Edge Computing have made it possible to automatically analyze streams of videos from home/security cameras over hierarchical clusters that include edge devices, close to the video source, as well as remote cloud compute resources. However, preserving the privacy and confidentiality of users' sensitive data as it passes through different devices remains a concern to most users. Private user data is subject to attacks by malicious attackers or misuse by internal administrators who may use the data in activities that are not explicitly approved by…
