Summary of Testing Credibility Of Public and Private Surveys Through the Lens Of Regression, by Debabrota Basu et al.
Testing Credibility of Public and Private Surveys through the Lens of Regression
by Debabrota Basu, Sourav Chakraborty, Debarshi Chanda, Buddha Dev Das, Arijit Ghosh, Arnab Ray
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Methodology (stat.ME); Machine Learning (stat.ML)
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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 The paper presents an algorithm that tests the credibility of a sample survey in terms of linear regression, ensuring the correctness of data analysis tools like linear regression. The algorithm is designed to certify whether a sample survey is good enough to guarantee correct results when using linear regression models. Additionally, the authors extend their approach to work with surveys published under Local Differential Privacy (LDP), a technique ensuring privacy for survey participants. They also propose an algorithm that learns linear regression models from data corrupted with noise from subexponential distributions. The paper theoretically proves the correctness of these algorithms and numerically demonstrates their performance on real and synthetic datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps make sure that sample surveys are a fair representation of what’s really going on in the whole population. They developed an algorithm to check if a survey is good enough to give accurate results when using tools like linear regression. The authors also looked at how this works with surveys that prioritize privacy, making sure participants don’t worry about their data being shared. They tested their ideas and showed they work well with real and made-up datasets. |
Keywords
» Artificial intelligence » Linear regression
