Summary of The Data Minimization Principle in Machine Learning, by Prakhar Ganesh et al.
The Data Minimization Principle in Machine Learning
by Prakhar Ganesh, Cuong Tran, Reza Shokri, Ferdinando Fioretto
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 A machine learning-based optimization framework is introduced to implement the principle of data minimization, which aims to reduce data collection, processing, or retention to minimize misuse, unauthorized access, or breaches. The framework is rooted in privacy-by-design principles and endorsed by global data protection regulations. However, its practical implementation has been hindered by the lack of a rigorous formulation until now. The optimization algorithms are adapted to perform data minimization, and their compliance with minimization objectives as well as impact on user privacy are evaluated comprehensively. The analysis highlights the mismatch between privacy expectations and actual benefits, emphasizing the need for approaches that account for multiple facets of real-world privacy risks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Data minimization is a way to keep our personal data safe by reducing how much we collect, process, or store it. This helps prevent misuse or unauthorized access. Many countries have laws supporting this idea, but making it happen has been tricky because there isn’t a clear formula for doing so. A new paper tries to fix that problem by creating an optimization framework that follows the rules of data minimization. It also tests different algorithms to see which one works best and how well they protect our privacy. The results show that we need better ways to keep our personal data safe because what we expect in terms of protection doesn’t always match what’s actually happening. |
Keywords
» Artificial intelligence » Machine learning » Optimization
