1. A study for Relational Database watermarking scheme Speaker: Pei-Feng Shiu Date: 2012/09/21

2. Outline Introduction Relational Database watermarking schemes Discussion 2012/9/211

3. Introduction Image –Pixel(0-255) Database –Textual data –Numerical data 2012/9/212 Pixel blocks Coefficient blocks Compression code Primary key Attribute1Attribute2Attribute3Attribute4 Tuple 11Alice334.1024Alice@gmail.com515203 Tuple 22Bob128.6427Bob@hotmail.com138667 Tuple 33Casey305.1839Casey@livemail.tw152470 … Tuple n

4. Schemes Relational Databases Watermarking for textual and numerical data –Zhang et al., MEC2011, China, 2011. DCT Transform based Relational Database Robust Watermarking Algorithm –Song et al., ISDPE2010, USA, 2010. 2012/9/213

5. 4 Relational Databases Watermarking for textual and numerical data 2011 International Conference on Mechatronic Science, Electric Engineering and Computer Author: Lizhong Zhang, Wei Gao, Nan Jiang, Liqiu Zhang and Yan Zhang

6. Notations and Parameters Parameter Description N N is a natural number e Number of groups in the relation Key User’s secret key r i.p Primary key of the ith tuple in relation or in group HiHi Tuple Hash of the ith tuple r i.mod The remainder of the ith tuple r i.A j the jth attribute value of the ith tuple in the relation Lt Watermark tag location of textual attributes Ln Watermark tag location of numerical attributes Number of candidate bits to be modified 2012/9/215

7. Watermark embedding algorithm Tuples selection. 2012/9/216 Condition: Primary key Attribute1Attribute2Attribute3Attribute4 Tuple 1 Tuple 2 Tuple 3 … Tuple n 40,000

8. Watermark embedding algorithm Watermark insertion 2012/9/217 Primary key Attribute1Attribute2Attribute3Attribute4 Tuple 235235Paul212.751akaily@livemail.tw99700 Textual attribute: Numerical attribute: If Watermark bit =1 then Insert #13(Carriage return character) at location Lt i,j Else Insert #10(Linefeed character) at location Lt i,j 155 mod 5 = 0 1 155 mod 4 = 3

9. Watermark extracting algorithm Tuples selection Watermark extraction 2012/9/218 Primary key Attribute1Attribute2Attribute3Attribute4 Tuple 235235P aul212.751akaily@livemail.tw99701 Textual attribute: Numerical attribute: Watermark bit = Ln i,j digits mod 2 If #13 at location Lt i,j then Watermark bit =1 If #10 at location Lt i,j then Watermark bit =0

10. Experimental results Subset Addition Attack –Watermark detected ratio 99.5% Subset Alteration Attack –Watermark detected ratio 99.5% even if 70% of the tuples of the database are modified Subset Deletion Attack –Watermark detected ratio 95% even if 70% of the tuples are deleted. 2012/9/219

11. 10 DCT Transform based Relational Database Robust Watermarking Algorithm 2010 Second International Symposium on Data, Privacy, and E-Commerce Author: Song Yige, Liu Weidong, Song Jiaxing and Wong Ming Sze Angela

12. Notations and Parameters ParameterDescription the primary key of the ith tuple in the relation or in a group user’s secret key tuple hash of the ith tuple tuple group number of the ith tuple gnumber of groups in the relation after divide snumber of attributes allowed tag in the relation watermark tag attribute location of the ith tuple CThe medium frequency coefficient in DCT 2012/9/2111

13. Watermark embedding algorithm Tuples selection 2012/9/2112 Primary key Attribute1Attribute2Attribute3Attribute4 Tuple 1 Tuple 2 Tuple 3 … Tuple n 581,012

14. Watermark embedding algorithm DCT transform - two-dimensional DCT Watermark insertion –Select medium frequency coefficient as C Inverse DCT transform Update database 2012/9/2113 Extracting phase: if C > 0 then Watermark bit = 0 else if C < 0 then Watermark bit = 1

15. Experimental results Subset Addition Attack 2012/9/2114 Original watermark and extracted watermark after tuple deletion attack

16. Experimental results Subset Alteration Attack 2012/9/2115 Original watermark and extracted watermark after tuple deletion attack

17. Experimental results Subset Deletion Attack 2012/9/2116 Original watermark and extracted watermark after tuple deletion attack

18. Discussion Practicality Affect data analysis result Affect data mining result Effect of computing time for big data 2012/9/2117

19. Introduction-reason Digital Media (Video, Audio, Images, Text) are easily copied and easily distributed via the web. Database outsourcing is a common practice: –Stock market data –Consumer Behavior data (Walmart) –Power Consumption data –Weather data Effective means for proof of authorship. –Signature and data are the same object. Effective means of tamper proofing. –Integrity information is embedded in the data. 2012/9/2118