Name
Abstract | ||
|---|---|---|
A novel method of rights protection for categorical data through watermarking is introduced in this paper. New watermark embedding channels are discovered and associated novel watermark encoding algorithms are proposed. While preserving data quality requirements, the introduced solution is designed to survive important attacks, such as subset selection and random alterations. Mark detection is fully "blind驴 in that it doesn't require the original data, an important characteristic, especially in the case of massive data. Various improvements and alternative encoding methods are proposed and validation experiments on real-life data are performed. Important theoretical bounds including mark vulnerability are analyzed. The method is proved (experimentally and by analysis) to be extremely resilient to both alteration and data loss attacks, for example, tolerating up to 80 percent data loss with a watermark alteration of only 25 percent. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/TKDE.2005.116 | Knowledge and Data Engineering, IEEE Transactions |
Keywords | Field | DocType |
original data,real-life data,percent data loss,important characteristic,important attack,categorical data,associated novel watermark,data quality requirement,rights protection,massive data,data loss attack,resists,indexing terms,access control,relational data,information hiding,data quality,data mining,relational databases,law,watermarking | Data mining,Steganography,Digital watermarking,Data quality,Data loss,Computer science,Categorical variable,Information hiding,Watermark,Encoding (memory) | Journal |
Volume | Issue | ISSN |
17 | 7 | 1041-4347 |
Citations | PageRank | References |
19 | 1.16 | 20 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Radu Sion | 1 | 1252 | 81.36 |
| Mikhail J. Atallah | 2 | 3828 | 340.54 |
| Sunil Prabhakar | 3 | 2664 | 152.75 |