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Mendeley readers
| Chapter title |
Evaluating the Privacy Implications of Frequent Itemset Disclosure
|
|---|---|
| Chapter number | 34 |
| Book title |
ICT Systems Security and Privacy Protection
|
| Published in |
ICT systems security and privacy protection : 32nd IFIP TC 11 International Conference, SEC 2017, Rome, Italy, May 29-31, 2017, Proceedings. IFIP TC11 International Information Security Conference (32nd : 2017 : Rome, Italy), May 2017
|
| DOI | 10.1007/978-3-319-58469-0_34 |
| Pubmed ID | |
| Book ISBNs |
978-3-31-958468-3, 978-3-31-958469-0
|
| Authors |
Edoardo Serra, Jaideep Vaidya, Haritha Akella, Ashish Sharma |
| Abstract |
Frequent itemset mining is a fundamental data analytics task. In many cases, due to privacy concerns, only the frequent itemsets are released instead of the underlying data. However, it is not clear how to evaluate the privacy implications of the disclosure of the frequent item-sets. Towards this, in this paper, we define the k-distant-IFM-solutions problem, which aims to find k transaction datasets whose pair distance is maximized. The degree of difference between the reconstructed datasets provides a way to evaluate the privacy risk. Since the problem is NP-hard, we propose a 2-approximate solution as well as faster heuristics, and evaluate them on real data. |
Mendeley readers
The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 1 | 100% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 1 | 100% |