Andreas Haeberlen

Ali Farahbakhsh, Qingjie Lu, Lorenzo Alvisi et al.

Metastable failures are hard to detect, prevent, and mitigate. During a metastable failure, a system exhibits self-sustaining bad behavior even in the absence of adversarial conditions. Prior work focuses on symptoms and has portrayed metastable failures as instances of self-sustaining overload. This characterization leaves the underlying failure causes and dynamics unknown, and does not account for metastable failures that do not manifest as overload. We present the first causal characterization of metastable failures by identifying their origin in metastable faults, i.e., structural destabilizing cycles of interaction among systems components that, in isolation, are stabilizing. Metastable failures arise when scheduling decisions let these destabilizing interactions gain the upper hand over the individual components' stabilizing tendencies. We then derive a methodology to predict metastable failures, and to build metastable-fault-tolerant (MFT) systems. We apply our methodology to three case studies, showcasing the generality of our results.

Hengchu Zhang, Edo Roth, Andreas Haeberlen et al.

Applying differential privacy at scale requires convenient ways to check that programs computing with sensitive data appropriately preserve privacy. We propose here a fully automated framework for {\em testing} differential privacy, adapting a well-known "pointwise" technique from informal proofs of differential privacy. Our framework, called DPCheck, requires no programmer annotations, handles all previously verified or tested algorithms, and is the first fully automated framework to distinguish correct and buggy implementations of PrivTree, a probabilistically terminating algorithm that has not previously been mechanically checked. We analyze the probability of DPCheck mistakenly accepting a non-private program and prove that, theoretically, the probability of false acceptance can be made exponentially small by suitable choice of test size. We demonstrate DPCheck's utility empirically by implementing all benchmark algorithms from prior work on mechanical verification of differential privacy, plus several others and their incorrect variants, and show DPCheck accepts the correct implementations and rejects the incorrect variants. We also demonstrate how DPCheck can be deployed in a practical workflow to test differentially privacy for the 2020 US Census Disclosure Avoidance System (DAS).