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Software side channel attacks have become a serious concern with the recent rash of attacks on speculative processor architectures. Most attacks that have been demonstrated exploit the cache tag state as their exfiltration channel. While many existing defense mechanisms that can be implemented solely in software have been proposed, these mechanisms appear to patch specific attacks, and can be circumvented. In this paper, we propose minimal modifications to hardware to defend against a broad class of attacks, including those based on speculation, with the goal of eliminating the entire attack surface associated with the cache state covert channel.We propose DAWG, Dynamically Allocated Way Guard, a generic mechanism for secure way partitioning of set associative structures including memory caches. DAWG endows a set associative structure with a notion of protection domains to provide strong isolation. When applied to a cache, unlike existing quality of service mechanisms such as Intel's Cache Allocation Technology (CAT), DAWG fully isolates hits, misses, and metadata updates across protection domains. We describe how DAWG can be implemented on a processor with minimal modifications to modern operating systems. We describe a noninterference property that is orthogonal to speculative execution and therefore argue that existing attacks such as Spectre Variant 1 and 2 will not work on a system equipped with DAWG. Finally, we evaluate the performance impact of DAWG on the cache subsystem.

Recent proposals for trusted hardware platforms, such as Intel SGX and the MIT Sanctum processor, offer compelling security features but lack formal guarantees. We introduce a verification methodology based on a trusted abstract platform (TAP), a formalization of idealized enclave platforms along with a parameterized adversary. We also formalize the notion of secure remote execution and present machine-checked proofs showing that the TAP satisfies the three key security properties that entail secure remote execution: integrity, confidentiality and secure measurement. We then present machine-checked proofs showing that SGX and Sanctum are refinements of the TAP under certain parameterizations of the adversary, demonstrating that these systems implement secure enclaves for the stated adversary models. CCS CONCEPTS• Security and privacy → Formal methods and theory of security; Security in hardware; Trusted computing; Information flow control;

During the secure boot process for a trusted execution environment, the processor must provide a chain of certificates to the remote client demonstrating that their secure container was established as specified. This certificate chain is rooted at the hardware manufacturer who is responsible for constructing chips according to the correct specification and provisioning them with key material. We consider a semi-honest manufacturer who is assumed to construct chips correctly, but may attempt to obtain knowledge of client private keys during the process.Using the RISC-V Rocket chip architecture as a base, we design, document, and implement an attested execution processor that does not require secure non-volatile memory, nor a private key explicitly assigned by the manufacturer. Instead, the processor derives its cryptographic identity from manufacturing variation measured by a Physical Unclonable Function (PUF). Software executed by a bootloader built into the processor transforms the PUF output into an elliptic curve key pair. The (re)generated private key is used to sign trusted portions of the boot image, and is immediately destroyed. The platform can therefore provide attestations about its state to remote clients. Reliability and security of PUF keys are ensured through the use of a trapdoor computational fuzzy extractor.We present detailed evaluation results for secure boot and attestation by a client of a Rocket chip implementation on a Xilinx Zynq 7000 FPGA.

We use reconfigurable hardware to construct a high throughput Bayesian computing machine (BCM) capable of evaluating probabilistic networks with arbitrary DAG (directed acyclic graph) topology. Our BCM achieves high throughput by exploiting the FPGA's distributed memories and abundant hardware structures (such as long carry-chains and registers), which enables us to 1) develop an innovative memory allocation scheme based on a maximal matching algorithm that completely avoids memory stalls, 2) optimize and deeply pipeline the logic design of each processing node, and 3) schedule them optimally. The BCM architecture not only can be applied to many important algorithms in artificial intelligence, signal processing, and digital communications, but also has high reusability, i.e., a new application needs not change a BCM's hardware design, only new task graph processing and code compilation are necessary. Moreover, the throughput of a BCM scales almost linearly with the size of the FPGA on which it is implemented.A Bayesian computing machine with 16 processing nodes was implemented with a Virtex-5 FPGA (XCV5LX155T-2) on a BEE3 (Berkeley Emulation Engine) platform. For a wide variety of sample Bayesian problems, comparing running the same network evaluation algorithm on a 2.4 GHz Core 2 Duo Intel processor and a GeForce 9400m using the CUDA software package, the BCM demonstrates 80x and 15x speedups respectively, with a peak throughput of 20.4 GFLOPS (Giga Floating-Point Operations per Second).

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