Name
Abstract | ||
|---|---|---|
We live in an era characterized by the abundance of data, often conveying personal information. Linking this kind of data is useful for a variety of applications, raising, however privacy concerns. To address this issue, privacy preserving record linkage has emerged, with techniques aiming at revealing to the matching parties only the actually matching records. Since the linking process usually involves large volumes of data, it is evident that such procedures could benefit from outsourcing computation to cloud infrastructures taking advantage of parallel computing platforms, such as Apache Spark. In this paper, we extend a phonetic codes based method for privacy preserving string matching, by designing a new protocol specifically tailored to operate in parallel in the cloud, employing the map reduce model. We theoretically analyze its characteristics and empirically assess its performance, comparing it with the corresponding sequential algorithm. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-69835-9_16 | ADVANCES ON P2P, PARALLEL, GRID, CLOUD AND INTERNET COMPUTING (3PGCIC-2017) |
DocType | Volume | ISSN |
Conference | 13 | 2367-4512 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexandros Karakasidis | 1 | 153 | 11.58 |
| Georgia Koloniari | 2 | 0 | 0.34 |