Trusted Execution Environments (TEEs) employ hardware-based isolation mechanisms to safeguard the confidentiality and integrity of sensitive code and data. One such prevalent implementation is Arm TrustZone, which partitions the system into the secure and normal (non-secure) worlds. However, this partitioning results in the secure world having very limited visibility into the operating information of the normal world, creating a semantic gap between these two worlds. Specifically, the secure world lacks an effective user identity authentication when receiving data requests from the normal world. Consequently, malicious Client Applications (CAs) in the normal world can deceive Trusted Applications (TAs) in the secure world by utilizing elaborate request parameters, compromising the sensitive data stored by other CAs. We systematically classify these Semantic Gap Vulnerabilities (SGVs) and propose a mate system for the TEE calledMaTEEto defend against SGVs.MaTEEutilizes Arm Pointer Authentication (PA) to bind each request to the corresponding CA's identity and then verifies the identity when the CA accesses sensitive data, thereby preventing malicious request forgery. In particular,MaTEEisolates sensitive data of different CAs without modifying existing CAs and TAs. Our evaluation demonstrates thatMaTEEsuccessfully defends against SGVs with a minimal runtime overhead (2.19%).

Public cloud platforms have employed Trusted Execution Environment (TEE) technology to provide confidential computing services. However, applications running on cloud TEEs are susceptible to rollback or forking attacks. Their states can be rolled back to an outdated version or split into multiple conflicting versions, violating state continuity. Existing solutions against these attacks either rely on centralized trust assumption (e.g., trusted server) or have limited performance (e.g., tens of state updates per second). In this paper, we introduce Narrator-Pro (an upgrade to the original Narrator), a secure and practical distributed system that utilizes blockchain technology and TEEs to provide high-performance state continuity protection for TEE applications in the cloud. Specifically, we use the blockchain to initialize the system, which lays down the decentralized trust base with minimal interaction overhead. Meanwhile, we leverage the distributed system composed of TEEs to provide fast and unlimited state updates. We have implemented a proof-of-concept of Narrator-Pro in Intel SGX and conducted extensive evaluations in both the WAN and the LAN. Our results show that in a LAN environment with 5 nodes, Narrator-Pro can support around 8k state updates per second with a latency of 3.58ms. This performance is 30x higher than ROTE and 70× higher than using a TPM counter.

Nowadays, collaborative learning is becoming a new trend to address the data scarcity issue. To prevent potential privacy leakage, some privacy-preservation collaborative learning schemes have been proposed with data encryption, but cannot handle the setting of different data contribution among data nodes and avoid the huge overhead of implementing over encrypted data. In this paper, we design a flexible and secure decentralized collaborative learning to achieve the contribution over data nodes, where each data node can specialize the contribution extent for collaborative learning. Besides, we provide a MPC-friendly collaborative layer for the lightweight privacy preservation. Our security analysis and experimental results demonstrate the security and superiority of our system, respectively.