eprint.iacr.org will be offline for approximately an hour
for routine maintenance at 11pm UTC on Tuesday, April 16. We lost some data between
April 12 and April 14, and some authors have been notified that they need to resubmit their papers.
You are looking at a specific version 20150705:180312 of this paper.
See the latest version.
Paper 2015/671
Privacy-preserving Frequent Itemset Mining for Sparse and Dense Data
Peeter Laud and Alisa Pankova
Abstract
Frequent itemset mining is a task that can in turn be used for other purposes such as associative rule mining. One problem is that the data may be sensitive, and its owner may refuse to give it for analysis in plaintext. There exist many privacy-preserving solutions for frequent itemset mining, but in any case enhancing the privacy inevitably spoils the efficiency. Leaking some less sensitive information such as data density might improve the efficiency. In this paper, we devise an approach that works better for sparse matrices and compare it to the related work that uses similar security requirements on similar secure multiparty computation platform.
Metadata
- Available format(s)
-
PDF
- Category
- Applications
- Publication info
- Preprint. MINOR revision.
- Keywords
- frequent itemset miningsecure multiparty computation
- Contact author(s)
- peeter @ cyber ee
- History
- 2015-07-05: received
- Short URL
- https://ia.cr/2015/671
- License
-
CC BY
