Name
Abstract | ||
|---|---|---|
In this paper, we give approximate algorithms for privacy preserving distance based outlier detection for both horizontal and vertical distributions, which scale well to large datasets of high dimensionality in comparison with the existing techniques. In order to achieve efficient private algorithms, we introduce an approximate outlier detection scheme for the centralized setting which is based on the idea of Locality Sensitive Hashing. We also give theoretical and empirical bounds on the level of approximation of the proposed algorithms. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICDMW.2011.141 | Data Mining Workshops |
Keywords | Field | DocType |
locality sensitive hashing,outlier detection,approximate outlier detection scheme,existing technique,large datasets,centralized setting,privacy preserving outlier detection,efficient private algorithm,empirical bound,approximate algorithm,high dimensionality,privacy,data mining,data privacy | Locality-sensitive hashing,Data mining,Anomaly detection,Horizontal and vertical,Locality preserving hashing,Computer science,Curse of dimensionality,Information privacy | Conference |
ISBN | Citations | PageRank |
978-1-4673-0005-6 | 1 | 0.35 |
References | Authors | |
6 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nisarg Raval | 1 | 68 | 5.85 |
| Madhuchand Rushi Pillutla | 2 | 5 | 0.78 |
| Piyush Bansal | 3 | 28 | 4.44 |
| Kannan Srinathan | 4 | 422 | 41.70 |
| C. V. Jawahar | 5 | 1700 | 148.58 |