LEMNA: explaining deep learning based security applications

LEMNA: explaining deep learning based security applications Guo et al., CCS'18 Understanding why a deep learning model produces the outputs it does is an important part of gaining trust in the model, and in some situations being able to explain decisions is a strong requirement. Today’s paper shows that by carefully considering the architectural features … Continue reading LEMNA: explaining deep learning based security applications

Towards usable checksums: automating the integrity verification of web downloads for the masses

Towards usable checksums: automating the integrity verification of web downloads for the masses Cherubini et al., CCS'18 If you tackled Monday’s paper on BEAT you deserve something a little easier to digest today, and ‘Towards usable checksums’ fits the bill nicely! There’s some great data-driven product management going on here as the authors set out … Continue reading Towards usable checksums: automating the integrity verification of web downloads for the masses

BEAT: asynchronous BFT made practical

BEAT: asynchronous BFT made practical Duan et al., CCS'18 Reaching agreement (consensus) is hard enough, doing it in the presence of active adversaries who can tamper with or destroy your communications is much harder still. That’s the world of Byzantine fault tolerance (BFT). We’ve looked at Practical BFT (PBFT) and HoneyBadger on previous editions of … Continue reading BEAT: asynchronous BFT made practical

Uncertainty propagation in data processing systems

Uncertainty propagation in data processing systems Manousakis et al., SoCC'18 When I’m writing an edition of The Morning Paper, I often imagine a conversation with a hypothetical reader sat in a coffee shop somewhere at the start of their day. There are three levels of takeaway from today’s paper choice: If you’re downing a quick … Continue reading Uncertainty propagation in data processing systems

Continuum: a platform for cost-aware low-latency continual learning

Continuum: a platform for cost-aware low-latency continual learning Tian et al., SoCC'18 Let’s start with some broad approximations. Batching leads to higher throughput at the cost of higher latency. Processing items one at a time leads to lower latency and often reduced throughput. We can recover throughput to a degree by throwing horizontally scalable resources … Continue reading Continuum: a platform for cost-aware low-latency continual learning

Overload control for scaling WeChat microservices

Overload control for scaling WeChat microservices Zhou et al., SoCC'18 There are two reasons to love this paper. First off, we get some insights into the backend that powers WeChat; and secondly the authors share the design of the battle hardened overload control system DAGOR that has been in production at WeChat for five years. … Continue reading Overload control for scaling WeChat microservices