mlsanityData to 7 Oct 2026

Collaborative Drug Discovery: Inference-level Data Protection Perspective

arXiv 2205.06506 ↗First posted 13 May 2022cs.CR cs.LGcited by 4

Not on the map. Why?

Abstract (arXiv)

Pharmaceutical industry can better leverage its data assets to virtualize drug discovery through a collaborative machine learning platform. On the other hand, there are non-negligible risks stemming from the unintended leakage of participants' training data, hence, it is essential for such a platform to be secure and privacy-preserving. This paper describes a privacy risk assessment for collaborative modeling in the preclinical phase of drug discovery to accelerate the selection of promising drug candidates. After a short taxonomy of state-of-the-art inference attacks we adopt and customize several to the underlying scenario. Finally we describe and experiments with a handful of relevant privacy protection techniques to mitigate such attacks.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 33 of its 51 references that are in the map. First posted on arXiv 13 May 2022.

Builds on

33 of 51 references.

References · 33

  1. 2019Advances and Open Problems in Federated Learningcited by 3,021
  2. 2016Deep Learning with Differential Privacycited by 2,682
  3. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  4. 2019Deep Leakage from Gradientscited by 1,004
  5. 2018How To Backdoor Federated Learningcited by 745
  6. 2020The Future of Digital Health with Federated Learningcited by 625
  7. 2016Stealing Machine Learning Models via Prediction APIscited by 602
  8. 2018Exploiting Unintended Feature Leakage in Collaborative Learningcited by 509
  9. 2018ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Modelscited by 475
  10. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 451
  11. 2017Deep Models Under the GAN: Information Leakage from Collaborative Deep Learningcited by 326
  12. 2021Membership Inference Attacks on Machine Learning: A Surveycited by 306
21 more
  1. 2019White-box vs Black-box: Bayes Optimal Strategies for Membership Inferencecited by 265
  2. 2018Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learningcited by 242
  3. 2019MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examplescited by 183
  4. 2019Stolen Memories: Leveraging Model Memorization for Calibrated White-Box Membership Inferencecited by 168
  5. 2020A Survey of Privacy Attacks in Machine Learningcited by 143
  6. 2019Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learningcited by 117
  7. 2015Generalization in Adaptive Data Analysis and Holdout Reusecited by 76
  8. 2018Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learningcited by 75
  9. 2020When Machine Learning Meets Privacy: A Survey and Outlookcited by 67
  10. 2017Knock Knock, Who's There? Membership Inference on Aggregate Location Datacited by 63
  11. 2019Free-riders in Federated Learning: Attacks and Defensescited by 48
  12. 2018Towards Demystifying Membership Inference Attackscited by 39
  13. 2019SoK: Differential Privaciescited by 33
  14. 2019Eavesdrop the Composition Proportion of Training Labels in Federated Learningcited by 27
  15. 2019Privacy for Free: Communication-Efficient Learning with Differential Privacy Using Sketchescited by 17
  16. 2020A Federated Learning Approach to Anomaly Detection in Smart Buildingscited by 17
  17. 2020Quality Inference in Federated Learning with Secure Aggregationcited by 15
  18. 2019Quantification of the Leakage in Federated Learningcited by 7
  19. 2017Together or Alone: The Price of Privacy in Collaborative Learningcited by 4
  20. 2019Quantifying the Privacy Risks of Learning High-Dimensional Graphical Modelscited by 3
  21. 2019Expressive Graph Informer Networkscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 2; 2025: 0; 2026: 1.

4 papers.

  1. 2022Industry-Scale Orchestrated Federated Learning for Drug Discoverycited by 19
  2. 2024Publishing Neural Networks in Drug Discovery Might Compromise Training Data Privacycited by 3
  3. 2024A Strong Baseline for Molecular Few-Shot Learningcited by 0
  4. 2026Conditional GraphGANFed: Optimizing Graph-Structured Molecule Generation in Federated Generative Adversarial Networkscited by 0