This post summarizes the SotA on the Privacy and Security of Machine Learning from my naive understanding. Corrections are highly appreciated.

**USENIX2021: **

Tracks:
Machine Learning: Backdoor and Poisoning
Machine Learning: Adversarial Examples and Model Extraction
Adversarial Machine Learning: Defenses
Machine Learning: Privacy Issues

Systematic Evaluation of Privacy Risks of Machine Learning Models
Keywords: Bayes’ theorem, class-dependent threshold

Defeating DNN-Based Traffic Analysis Systems in Real-Time With Blind Adversarial Perturbations
Keywords: constrained domain, DNN, network traffic, Tor

Extracting Training Data from Large Language Models
Keywords: Privacy, GPT-2 (only public data – safe attack), model extraction, sensitive data, model/neural network memorization, differential privacy

Explanation-Guided Backdoor Poisoning Attacks Against Malware Classifiers

Others

CADE: Detecting and Explaining Concept Drift Samples for Security Applications
Keywords: concept drift, android malware, distance-based explanation

Mind Your Weight(s): A Large-scale Study on Insufficient Machine Learning Model Protection in Mobile Apps
Keywords: model protection, mobile, apps