Many machine learning (ML) models are Python pickle files under the hood, and it makes sense. The use of pickling conserves memory, enables start-and-stop model training, and makes trained models portable (and, thereby, shareable). Pickling is easy to implement, is built into Python without requiring additional dependencies, and supports serialization of custom […]
Trail of Bits has manually curated a wealth of data—years of security assessment reports—and now we’re exploring how to use this data to make the smart contract auditing process more efficient with Slither-simil. Based on accumulated knowledge embedded in previous audits, we set out to detect similar vulnerable code snippets […]
If you work on deep learning systems, check out our new tool, PrivacyRaven—it’s a Python library that equips engineers and researchers with a comprehensive testing suite for simulating privacy attacks on deep learning systems. Because deep learning enables software to perform tasks without explicit programming, it’s become ubiquitous in […]
During my internship this summer, I built a multi-party computation (MPC) tool that implements a 3-party computation protocol for perceptron and support vector machine (SVM) algorithms. MPC enables multiple parties to perform analyses on private datasets without sharing them with each other. I defveloped a technique that lets three parties obtain the results of machine […]
