Summary of Privacy Drift: Evolving Privacy Concerns in Incremental Learning, by Sayyed Farid Ahamed et al.
Privacy Drift: Evolving Privacy Concerns in Incremental Learning
by Sayyed Farid Ahamed, Soumya Banerjee, Sandip Roy, Aayush Kapoor, Marc Vucovich, Kevin Choi, Abdul Rahman, Edward Bowen, Sachin Shetty
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces the concept of “privacy drift” in Federated Learning (FL), a framework that parallels concept drift. While concept drift addresses changes in model accuracy over time due to changing data, privacy drift examines the variation in private information leakage as models undergo incremental training. The study aims to unveil the relationship between model performance evolution and data privacy integrity. Through experimentation on customized datasets derived from CIFAR-100, this paper investigates how model updates and data distribution shifts influence the susceptibility of models to membership inference attacks (MIA). Results show a complex interplay between model accuracy and privacy safeguards, revealing that enhancements in model performance can lead to increased privacy risks. The work lays the groundwork for future research on privacy-aware machine learning, aiming to balance model accuracy and data privacy in decentralized environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make sure private information stays private when training models without collecting all the data in one place. It’s called “privacy drift” and it’s like a shift in how much private info leaks out as the model gets better. The study looks at how this affects our ability to keep data safe from attacks, like trying to figure out who someone is based on their likes. They found that making models better can actually make them more vulnerable to these kinds of attacks. This work helps us understand how to balance making good models and keeping our private info safe. |
Keywords
* Artificial intelligence * Federated learning * Inference * Machine learning
