TrustZone : Integrated Hardware and Software Security

By T. Alves. 2004

511 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware (2018, background): “g, Intel SGX [34], ARM TrustZone [1] or Sanctum [11] offer a more pragmatic solution to this dearth of trust.”
M2R: Enabling Stronger Privacy in MapReduce Computation (2015, methodology): “To enable privacy-preserving computation, a range of security primitives have sur-faced recently, including trusted computing support for hardware-isolated computation [2, 5, 38, 40] as well as purely cryptographic techniques [20,21,47].”
Towards Scaling Blockchain Systems via Sharding (2018, methodology): “node in the system is provisioned with TEEs. We leverage Intel SGX in our implementations, but our design can work with any other TEE instantiations, for example hardware-based TEEs such as TrustZone [8], Sanctum [18], TPMs [5], or software-based TEEs such as Overshadow [16]. Threatmodel.The attacke”
Intel SGX Explained (2016, background): “ARM’s TrustZone [13] is a collection of hardware modules that can be used to conceptually partition a system’s resources between a secure world, which hosts a secure container, and a normal world, which runs an untrusted software stack.”
The gem5 Simulator: Version 20.0+ (2020, background): “tly, gem5 supports all of the above instruction sets and the interworking between them. On top of the user-level features, several important system-level extensions, e.g. the security (aka TrustZone® [9]) and virtualization extensions [12], have been contributed opening up new avenues for architectu”
SGX-Shield: Enabling Address Space Layout Randomization for SGX Programs (2017, background): “While much commodity hardware, including Intel SGX [30, 39] and ARM TrustZone [8],provide Trusted Execution Environments (TEEs), Sancus [43] designs a hardware architecture for TEEs.”
Shredder: Learning Noise Distributions to Protect Inference Privacy (2019, methodology): “Several research propose runningmachine learning algorithms in in trusted execution environments such as Intel SGX [63] andARMTrustZone [64] to address the same remote inference privacy [65–68] as well as integrity [65].”
Foreshadow: Extracting the Keys to the Intel SGX Kingdom with Transient Out-of-Order Execution (2018, unknown): “In response to these challenges, recent research [11,41,48] and industry efforts [1,2,35,43] developed Trusted Execution Environments (TEEs) that feature an alternative, non-hierarchical protection model for isolated application compartments called enclaves.”
Formal Abstractions for Attested Execution Secure Processors (2017, methodology): “have been made to commercialize trusted hardware such as TPMs [1], Arm’s Trustzone [5, 7], and Intel’s SGX [6, 51].”
A Formal Foundation for Secure Remote Execution of Enclaves (2017, background): “ARM TrustZone [1] implements a secure and normal mode of execution to effectively create a single privileged enclave in an isolated address space.”
Shredder: Learning Noise to Protect Privacy with Partial DNN Inference on the Edge (2019, methodology): “Several research propose running machine learning algorithms in in trusted execution environments such as Intel SGX [55] and ARM TrustZone [56] to address the same remote inference privacy [57, 58, 59, 60] as well as integrity [57].”
DarKnight: A Data Privacy Scheme for Training and Inference of Deep Neural Networks (2020, background): “Trusted execution environments such as ARMTrustZone[11], Intel SGX[12] and Sanctum[13] provide an execution environment where computational integrity of user’s application is guaranteed by the hardware.”
Building Verifiable Trusted Path on Commodity x86 Computers (2012, background): “ARM’s TrustZone Security Extensions [1] split a single physical processor state to safely and efficiently execute code in two separate worlds: a more-privileged secure world, and a normal world.”
Privacy-Preserving Inference in Machine Learning Services Using Trusted Execution Environments (2019, methodology): “phic DL models, which is a severe limitation in user facing services. Another technique to protect conﬁdentiality is leveraging Trusted Execution Environments (TEE) like Intel SGX [26], ARM TrustZone [1] or Sanctum [7]. Classic techniques like data encryption can protect the data during it’s storage”
Aegis: A Single-Chip Secure Processor (2007, methodology): “B[1] B[2] B[3] B[4] 128 bits 128 bits 128 bits 128 bits ( V, Address,”