AI privacy toolkit
The need to analyse personal data to drive business alongside the requirement to preserve the privacy of data subjects creates a known tension. Data protection regulations such as GDPR and CCPA define strict restrictions and obligations on the collection and processing of personal data. These are al...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-05-01
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| Series: | SoftwareX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352711023000481 |
| _version_ | 1797818633047506944 |
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| author | Abigail Goldsteen Ola Saadi Ron Shmelkin Shlomit Shachor Natalia Razinkov |
| author_facet | Abigail Goldsteen Ola Saadi Ron Shmelkin Shlomit Shachor Natalia Razinkov |
| author_sort | Abigail Goldsteen |
| collection | DOAJ |
| description | The need to analyse personal data to drive business alongside the requirement to preserve the privacy of data subjects creates a known tension. Data protection regulations such as GDPR and CCPA define strict restrictions and obligations on the collection and processing of personal data. These are also relevant for machine learning models, which can be used to derive personal information about their training sets. The open-source ai-privacy-toolkit is designed to help organizations navigate this challenging area and build more trustworthy AI solutions, with tools that protect privacy and help ensure the compliance of AI models. |
| first_indexed | 2024-03-13T09:10:50Z |
| format | Article |
| id | doaj.art-40482533fc9e4bbea78db1146c0eb3e9 |
| institution | Directory Open Access Journal |
| issn | 2352-7110 |
| language | English |
| last_indexed | 2024-03-13T09:10:50Z |
| publishDate | 2023-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | SoftwareX |
| spelling | doaj.art-40482533fc9e4bbea78db1146c0eb3e92023-05-27T04:25:48ZengElsevierSoftwareX2352-71102023-05-0122101352AI privacy toolkitAbigail Goldsteen0Ola Saadi1Ron Shmelkin2Shlomit Shachor3Natalia Razinkov4Corresponding author.; IBM Research, University of Haifa Campus, Haifa, IsraelIBM Research, University of Haifa Campus, Haifa, IsraelIBM Research, University of Haifa Campus, Haifa, IsraelIBM Research, University of Haifa Campus, Haifa, IsraelIBM Research, University of Haifa Campus, Haifa, IsraelThe need to analyse personal data to drive business alongside the requirement to preserve the privacy of data subjects creates a known tension. Data protection regulations such as GDPR and CCPA define strict restrictions and obligations on the collection and processing of personal data. These are also relevant for machine learning models, which can be used to derive personal information about their training sets. The open-source ai-privacy-toolkit is designed to help organizations navigate this challenging area and build more trustworthy AI solutions, with tools that protect privacy and help ensure the compliance of AI models.http://www.sciencedirect.com/science/article/pii/S2352711023000481Machine learningArtificial intelligencePrivacyComplianceOpen-sourceTrustworthy AI |
| spellingShingle | Abigail Goldsteen Ola Saadi Ron Shmelkin Shlomit Shachor Natalia Razinkov AI privacy toolkit SoftwareX Machine learning Artificial intelligence Privacy Compliance Open-source Trustworthy AI |
| title | AI privacy toolkit |
| title_full | AI privacy toolkit |
| title_fullStr | AI privacy toolkit |
| title_full_unstemmed | AI privacy toolkit |
| title_short | AI privacy toolkit |
| title_sort | ai privacy toolkit |
| topic | Machine learning Artificial intelligence Privacy Compliance Open-source Trustworthy AI |
| url | http://www.sciencedirect.com/science/article/pii/S2352711023000481 |
| work_keys_str_mv | AT abigailgoldsteen aiprivacytoolkit AT olasaadi aiprivacytoolkit AT ronshmelkin aiprivacytoolkit AT shlomitshachor aiprivacytoolkit AT nataliarazinkov aiprivacytoolkit |