Privacy via the Johnson-Lindenstrauss Transform
Suppose that party A collects private information about its users, where each user's data is represented as a bit vector. Suppose that party B has a proprietary data mining algorithm that requires estimating the distance between users, such as clustering or nearest neighbors. We ask if it is p...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Labor Dynamics Institute
2013-08-01
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| Series: | The Journal of Privacy and Confidentiality |
| Subjects: | |
| Online Access: | https://journalprivacyconfidentiality.org/index.php/jpc/article/view/625 |