Diverse Community Data for Benchmarking Data Privacy Algorithms
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929259692425216 |
|---|---|
| author | Sen, Aniruddha Task, Christine Kapur, Dhruv Howarth, Gary Bhagat, Karan |
| author_facet | Sen, Aniruddha Task, Christine Kapur, Dhruv Howarth, Gary Bhagat, Karan |
| contents | The Collaborative Research Cycle (CRC) is a National Institute of Standards and Technology (NIST) benchmarking program intended to strengthen understanding of tabular data deidentification technologies. Deidentification algorithms are vulnerable to the same bias and privacy issues that impact other data analytics and machine learning applications, and can even amplify those issues by contaminating downstream applications. This paper summarizes four CRC contributions: theoretical work on the relationship between diverse populations and challenges for equitable deidentification; public benchmark data focused on diverse populations and challenging features; a comprehensive open source suite of evaluation metrology for deidentified datasets; and an archive of more than 450 deidentified data samples from a broad range of techniques. The initial set of evaluation results demonstrate the value of these tools for investigations in this field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_13216 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Diverse Community Data for Benchmarking Data Privacy Algorithms Sen, Aniruddha Task, Christine Kapur, Dhruv Howarth, Gary Bhagat, Karan Cryptography and Security Machine Learning The Collaborative Research Cycle (CRC) is a National Institute of Standards and Technology (NIST) benchmarking program intended to strengthen understanding of tabular data deidentification technologies. Deidentification algorithms are vulnerable to the same bias and privacy issues that impact other data analytics and machine learning applications, and can even amplify those issues by contaminating downstream applications. This paper summarizes four CRC contributions: theoretical work on the relationship between diverse populations and challenges for equitable deidentification; public benchmark data focused on diverse populations and challenging features; a comprehensive open source suite of evaluation metrology for deidentified datasets; and an archive of more than 450 deidentified data samples from a broad range of techniques. The initial set of evaluation results demonstrate the value of these tools for investigations in this field. |
| title | Diverse Community Data for Benchmarking Data Privacy Algorithms |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2306.13216 |