Data, when coupled with state-of-the-art machine learning models, can enable remarkable applications. But, there exists an underlying tension: users wish to keep their data private, and model providers wish to protect their intellectual property. Homomorphic encryption (HE) and multi-party computation (MPC) techniques have been proposed as solutions to this problem; however, both techniques require model providers to fully trust the server performing the machine learning computation. This limits the scale of inference applications, since it prevents model providers from leveraging shared public cloud infrastructures.In this work, we present Chex-Mix, a solution to the problem of privacy-preserving machine learning between two mutually distrustful parties in an untrusted cloud setting. Chex-Mix relies on a combination of HE and trusted execution environments (TEEs), using HE to provide clients with confidentiality guarantees, and TEEs to provide model providers with confidentiality guarantees and protect the integrity of computation from malicious cloud adversaries. Unlike prior solutions to this problem, such as multi-key HE, single-key HE, MPC, or TEE-only techniques, our solution assumes that both the client and the cloud can be malicious, makes no collusion assumptions, and frees model providers from needing to maintain private online infrastructures. Our results show that Chex-Mix can execute at high efficiency, with low communication cost, while providing security guarantees unaddressed by prior work. Compared to a recent multi-key HE work that allows partial cloud offload, for example, Chex-Mix achieves a 3× lower communication cost and a 3× faster computation time.

Designers are increasingly using mixed-criticality networks in embedded systems to reduce size, weight, power, and cost. Perhaps the most successful of these technologies is Time-Triggered Ethernet (TTE), which lets critical time-triggered (TT) traffic and non-critical best-effort (BE) traffic share the same switches and cabling. A key aspect of TTE is that the TT part of the system is isolated from the BE part, and thus BE devices have no way to disrupt the operation of the TTE devices. This isolation allows designers to: (1) use untrusted, but low cost, BE hardware, (2) lower BE security requirements, and (3) ignore BE devices during safety reviews and certification procedures.We present PCSPOOF, the first attack to break TTE’s isolation guarantees. PCSPOOF is based on two key observations. First, it is possible for a BE device to infer private information about the TT part of the network that can be used to craft malicious synchronization messages. Second, by injecting electrical noise into a TTE switch over an Ethernet cable, a BE device can trick the switch into sending these malicious synchronization messages to other TTE devices. Our evaluation shows that successful attacks are possible in seconds, and that each successful attack can cause TTE devices to lose synchronization for up to a second and drop tens of TT messages — both of which can result in the failure of critical systems like aircraft or automobiles. We also show that, in a simulated spaceflight mission, PCSPOOF causes uncontrolled maneuvers that threaten safety and mission success. We disclosed PCSPOOF to aerospace companies using TTE, and several are implementing mitigations from this paper.

Real-time embedded systems perform many important functions in the modern world. A standard way to tolerate faults in these systems is with Byzantine fault-tolerant (BFT) state machine replication (SMR), in which multiple replicas execute the same software and their outputs are compared by the actuators. Unfortunately, traditional BFT SMR protocols are slow , requiring replicas to exchange sensor data back and forth over multiple rounds in order to reach agreement before each execution. The state of the art in reducing the latency of BFT SMR is eager execution , in which replicas execute on data from different sensors simultaneously on different processor cores. However, this technique results in 3–5× higher computation overheads compared to traditional BFT SMR systems, significantly limiting schedulability. We present CrossTalk , a new BFT SMR protocol that leverages the prevalence of redundant switched networks in embedded systems to reduce latency without added computation. The key idea is to use specific algorithms to move messages between redundant network planes (which many systems already possess) as the messages travel from the sensors to the replicas. As a result, CrossTalk can ensure agreement automatically in the network, avoiding the need for any communication between replicas. Our evaluation shows that CrossTalk improves schedulability by 2.13–4.24× over the state of the art. Moreover, in a NASA simulation of a real spaceflight mission, CrossTalk tolerates more faults than the state of the art while using nearly 3× less processor time.

Critical real-time systems like spacecraft and aircraft commonly use Byzantine fault-tolerant (BFT) state machine replication (SMR) to mask faulty processors and sensors. Unfortunately, existing BFT SMR techniques require replicas to reach agreement on redundant sensor data and perform source selection before executing, which adds unavoidable latency to every execution and inevitably decreases control performance. The standard way to reduce the latency of BFT SMR in nonreal-time systems is to use speculation, forgoing agreement on inputs altogether, and repeating executions when faults occur. However, this approach is not suitable for real-time systems, since its worst-case latency when faults occur can be even higher than that of non-speculative systems. This paper presents IGOR, a new speculative BFT SMR approach that leverages multi-core processors to avoid the added latency inherent to traditional BFT SMR techniques in both the absence and presence of faults. The key idea of IGOR is to eagerly execute on data from redundant sensors in parallel. While these executions are underway, the replicas reach agreement on which execution will determine the system's final state. As a result, IGOR'S latency is reduced to the time taken by the executions or by the agreement process, whichever is longer. Our evaluation shows that IGOR reduces latency by up to 1.75× and improves schedulability by 1.88-3.22× compared to the state of the art. We also show that when used to execute spacecraft guidance, navigation, and control software during a dynamic mission phase, IGOR noticeably increases vehicle stability.