Summary of Streaming Private Continual Counting Via Binning, by Joel Daniel Andersson et al.
Streaming Private Continual Counting via Binning
by Joel Daniel Andersson, Rasmus Pagh
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Data Structures and Algorithms (cs.DS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers tackle the challenge of maintaining differential privacy in continual observation settings, where data is released one element at a time. They focus on the special case of continual counting, seeking to approximate a sum of binary input elements while preserving privacy. Factorization mechanisms are the leading approach, but they require space proportional to the size of the input, making them impractical for streaming settings. The authors propose a simple binning-based method that approximates factorization mechanisms in low space, achieving provable sublinear space guarantees for certain types of matrices. Their approach outperforms asymptotically optimal factorization mechanisms in empirical experiments, even with very low space usage. This work extends the applicability of differential privacy to streaming settings and has implications for implementations of differentially private stochastic gradient descent. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to keep personal information private when we’re constantly adding new data to a database. Imagine you’re sharing your daily steps with friends, and they want to know how many steps you’ve taken in total over time. The problem is that if they can see all the individual steps, they might be able to figure out something about you that you don’t want them to know. To solve this issue, researchers have developed ways to release information about a dataset gradually, without revealing too much. One approach uses factorization mechanisms, but these require a lot of space and aren’t suitable for large datasets. The authors propose an alternative method called binning, which allows them to approximate the original mechanism in low space while still maintaining privacy. Their findings have implications for how we protect personal data online. |
Keywords
» Artificial intelligence » Stochastic gradient descent
