Regulations increasingly call for various assurances from machine learning (ML) model providers about their training data, training process, and model behavior. For better transparency, industry (e.g., Huggingface and Google) has adopted model cards and datasheets to describe various properties of training datasets and models. In the same vein, we introduce the notion of inference cards to describe the properties of a given inference (e.g., binding of the output to the model and its corresponding input). We coin the term ML property cards to collectively refer to these various types of cards.

Autonomous collaborative networks of devices are rapidly emerging in numerous domains, such as self-driving cars, smart factories, critical infrastructure, and Internet of Things in general. Although autonomy and self-organization are highly desired properties, they increase vulnerability to attacks. Hence, autonomous networks need dependable mechanisms to detect malicious devices in order to prevent compromise of the entire network. However, current mechanisms to detect malicious devices either require a trusted central entity or scale poorly.

Consensus mechanisms used by popular distributed ledgers are highly scalable but notoriously inefficient. Byzantine fault tolerance (BFT) protocols are efficient but far less scalable. Speculative BFT protocols such as Zyzzyva and Zyzzyva5 are efficient and scalable but require a trade-off: Zyzzyva requires only 3f + 1 replicas to tolerate f faults, but even a single slow replica will make Zyzzyva fall back to more expensive non-speculative operation. Zyzzyva5 does not require a non-speculative fallback, but requires 5f + 1 replicas in order to tolerate f faults. BFT variants using hardware-assisted trusted components can tolerate a greater proportion of faults, but require that every replica have this hardware. We present SACZyzzyva, addressing these concerns: resilience to slow replicas and requiring only 3f + 1 replicas, with only one replica needing an active monotonic counter at any given time. We experimentally evaluate our protocols, demonstrating low latency and high scalability. We prove that SACZyzzyva is optimally robust and that trusted components cannot increase fault tolerance unless they are present in at least two-thirds of replicas.

Abstract Deniable messaging protocols allow two parties to have ‘off-the-record’ conversations without leaving any record that can convince external verifiers about what either of them said during the conversation. Recent events like the Podesta email dump underscore the importance of deniable messaging to politicians, whistleblowers, dissidents and many others. Consequently, messaging protocols like Signal and OTR are designed with cryptographic mechanisms to ensure deniable communication, irrespective of whether the communications partner is trusted. Many commodity devices today support hardware-assisted remote attestation which can be used to convince a remote verifier of some property locally observed on the device. We show how an adversary can use remote attestation to undetectably generate a non-repudiable transcript from any deniable protocol (including messaging protocols) providing sender authentication, proving to skeptical verifiers what was said. We describe a concrete implementation of the technique using the Signal messaging protocol. We then show how to design protocols that are deniable even against an adversary capable of attestation, and in particular how attestation itself can be used to restore deniability by thwarting realistic classes of adversary.

In this paper, we explicate the suggested benefits of Clifford's geometric algebra (GA) when applied to the field of electrical engineering. Engineers are always interested in keeping formulas as simple or compact as possible, and we illustrate that geometric algebra does provide such a simplified representation in many cases. We also demonstrate an additional structural check provided by GA for formulas in addition to the usual checking of physical dimensions. Naturally, there is an initial learning curve when applying a new method, but it appears to be worth the effort, as we show significantly simplified formulas, greater intuition, and improved problem solving in many cases.