We report below the 43 papers which were not included in Table IV of our position paper.

Specifically, these papers are those that were included in the Proceedings of either: ACM Conference on Computer and Communications Security (CCS), IEEE Symposium on Security and Privacy (SP), Network and Distributed Systems Security Symposium (NDSS), USENIX Security Symposium (SEC) – between 2019 and 2021.

All such works were somewhat related to machine learning and cybersecurity, but fell outside the scope of our position paper (refer to Appendix B-A for an explanation).

Year Venue Authors Title (+link)
2019 NDSS Hagestedt et al. MBeacon: Privacy-Preserving Beacons for DNA Methylation Data
2019 SP Zheng et al. Helen: Maliciously Secure Coopetitive Learning for Linear Models
2019 SP Melis et al. Exploiting Unintended Feature Leakage in Collaborative Learning
2019 SEC Mirsky et al. CT-GAN: Malicious Tampering of 3D Medical Imagery using Deep Learning
2019 CCS Agraval et al. QUOTIENT: Two-Party Secure Neural Network Training and Prediction
2019 CCS Baluta et al. Quantitative Verification of Neural Networks and Its Security Applications
2019 CCS Du et al. Lifelong Anomaly Detection Through Unlearning
2020 NDSS Bohler et al. Secure Sublinear Time Differentially Private Median Computation
2020 NDSS Patra et al. BLAZE: Blazing Fast Privacy-Preserving Machine Learning
2020 NDSS Chaudari et al. Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning
2020 SEC Shan et al. Fawkes: Protecting Privacy against Unauthorized Deep Learning Models
2020 SEC Fang et al. Local Model Poisoning Attacks to Byzantine-Robust Federated Learning
2020 SEC Pan et al. Justinian’s GAAvernor: Robust Distributed Learning with Gradient Aggregation Agent
2020 SP Pan et al. Privacy Risks of General-Purpose Language Models
2020 SP Wu et al. The Value of Collaboration in Convex Machine Learning with Differential Privacy
2020 SP Hasan et al. BLAZE: Blazing Fast Privacy-Preserving Machine Learning
2020 SP Kumar et al. CryptFlow: Secure TensorFlow Inference
2020 CCS Rathee et al. CryptFlow2: Practical 2-Party Secure Inference
2021 NDSS Ma et al. Let’s Stride Blindfolded in a Forest: Sublinear Multi-Client Decision Trees Evaluation
2021 NDSS Zhang et al. GALA: Greedy ComputAtion for Linear Algebra in Privacy-Preserved Neural Networks
2021 NDSS Liang et al. FARE: Enabling Fine-grained Attack Categorization under Low-quality Labeled Data
2021 NDSS Sav et al. POSEIDON: Privacy-Preserving Federated Neural Network Learning
2021 NDSS Cao et al. FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping
2021 NDSS Shejwalkar and Houmansadr Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning
2021 NDSS Le et al. CV-INSPECTOR: Towards Automating Detection of Adblock Circumvention
2021 NDSS Zimmeck et al. PrivacyFlash Pro: Automating Privacy Policy Generation for Mobile Apps
2021 NDSS Vishvamitra et al. Towards Understanding and Detecting Cyberbullying in Real-world Images
2021 SP Bourtoule et al. Machine Unlearning
2021 SP Abdullah et al. SoK: The Faults in our ASRs: An Overview of Attacks against Automatic Speech Recognition and Speaker Identification Systems
2021 SP Roy et al. Learning Differentially Private Mechanisms
2021 SP Tan et al. CRYPTGPU: Fast Privacy-Preserving Machine Learning on the GPU
2021 SP Cheu et al. Manipulation Attacks in Local Differential Privacy
2021 SP Rathee et al. SIRNN: A Math Library for Secure RNN Inference
2021 SP Jia et al. Proof-of-Learning: Definitions and Practice
2021 SEC Zheng et al. Cerebro: A Platform for Multi-Party Cryptographic Collaborative Learning
2021 SEC Zhang et al. Leakage of Dataset Properties in Multi-Party Machine Learning
2021 SEC Koti et al. SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning
2021 SEC Chen et al. Cost-Aware Robust Tree Ensembles for Security Applications
2021 SEC Yang et al. CADE: Detecting and Explaining Concept Drift Samples for Security Applications
2021 SEC Sun et al. Mind Your Weight(s): A Large-scale Study on Insufficient Machine Learning Model Protection in Mobile Apps
2021 CCS Han et al. DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications
2021 CCS Zhao et al. AI-Lancet: Locating Error-inducing Neurons to Optimize Neural Networks
2021 CCS Chen et al. Learning Security Classifiers with Verified Global Robustness Properties
2021 CCS Malekzadeh et al. Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers’ Outputs