The VLDB Journal

, Volume 19, Issue 4, pp 531–556 | Cite as

Rights protection of trajectory datasets with nearest-neighbor preservation

  • Claudio Lucchese
  • Michail Vlachos
  • Deepak Rajan
  • Philip S. Yu
Regular Paper

Abstract

Companies frequently outsource datasets to mining firms, and academic institutions create repositories or share datasets in the interest of promoting research collaboration. Still, many practitioners have reservations about sharing or outsourcing datasets, primarily because of fear of losing the principal rights over the dataset. This work presents a way of convincingly claiming ownership rights over a trajectory dataset, without, at the same time, destroying the salient dataset characteristics, which are important for accurate search operations and data-mining tasks. The digital watermarking methodology that we present distorts imperceptibly a collection of sequences, effectively embedding a secret key, while retaining as well as possible the neighborhood of each object, which is vital for operations such as similarity search, classification, or clustering. A key contribution in this methodology is a technique for discovering the maximum distortion that still maintains such desirable properties. We demonstrate both analytically and empirically that the proposed dataset marking techniques can withstand a number of attacks (such a translation, rotation, noise addition, etc) and therefore can provide a robust framework for facilitating the secure dissemination of trajectory datasets.

Keywords

Trajectories Time-series Watermarking Rights protection Nearest neighbors 

Notation

\({\mathcal{D}}\)

Original dataset of trajectories

\({\widehat{\mathcal{D}}}\)

Watermarked dataset

x

Trajectory in time-domain

X

Trajectory in frequency domain

n

Number of points in a sequence

\({X_j = \rho_j e ^ {\phi_j i}}\)

Fourier descriptor as a function of its magnitude and phase

p

Embedding power

\({\widehat{X_j} = \widehat{\rho_j}e^{\widehat{\phi_j}i}}\)

Watermarked Fourier descriptor as a function of its watermarked magnitude and phase

\({\mu_j(\mathcal{D})}\)

Mean of ρ j across the trajectories in \({\mathcal{D}}\)

l

Number of non-zero elements of watermark

χ

Correlation

\({\widehat{D}_p(x,y)}\)

Distance between two trajectories x, y after watermarking with power p

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agarwal, P., Adi, K., Prabhakaran, B.: Robust blind watermarking mechanism for motion data streams. In: Proceedings of ACM Workshop on Multimedia and Security, pp. 230–235 (2006)Google Scholar
  2. 2.
    Agarwal, P., Prabhakaran, B.: Tamper proofing mechanisms for motion capture data. In: Proceedings of ACM Workshop on Multimedia and Security, pp. 91–100 (2008)Google Scholar
  3. 3.
    Aggarwal, C.C., Yu, P.S.: A condensation approach to privacy preserving data mining. In: Proceedings of EDBT, pp. 183–199 (2004)Google Scholar
  4. 4.
    Agrawal, R., Kiernan, J.: Watermarking relational databases. In: Proceedings of VLDB, pp. 155–166 (2002)Google Scholar
  5. 5.
    Agrawal, R., Srikant, R.: Privacy preserving data mining. In: Proceedings of SIGMOD, pp. 439–450 (2000)Google Scholar
  6. 6.
    Aha D., Kibler D., Albert M.: Instance based learning algorithms. Mach. Learn. 6(1), 37–66 (1991)Google Scholar
  7. 7.
    Atkeson C.G., Moore A.W., Schaal S.: Locally weighted learning. Artif. Intell. Rev. 11(1–5), 11–73 (1997)CrossRefGoogle Scholar
  8. 8.
    Bassia, P., Pitas, I.: Robust audio watermarking in the time domain. In: European Signal Processing Conference (EUSIPCO) (1998)Google Scholar
  9. 9.
    Becker, M., Desoky, A.: A study of the DVD content scrambling system (CSS) algorithm. In: Proceedings of IEEE International Symposium on Signal Processing and Information Technology, pp. 353–356 (2004)Google Scholar
  10. 10.
    Bertino E., Khan L.R., Sandhu R.S., Thuraisingham B.M.: Secure knowledge management: confidentiality, trust, and privacy. IEEE Trans. Syst. Man Cybern. A 36(3), 429–438 (2006)CrossRefGoogle Scholar
  11. 11.
    Brickell, J., Shmatikov, V.: The cost of privacy: destruction of data-mining utility in anonymized data publishing. In: Proceedings of SIGKDD, pp. 70–78 (2008)Google Scholar
  12. 12.
    Chen B., Wornell G.: Quantization index modulation: a class of provably good methods for digital watermarking and information embedding. IEEE Trans. Inf. Theory 47(4), 1423–1443 (2001)MATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Chen, B., Wornell, G.W.: Achievable performance of digital watermarking systems. In: IEEE International Conference on Multimedia Computing and Systems, pp. 13–18 (1999)Google Scholar
  14. 14.
    Chen, K., Liu, L.: Privacy preserving data classification with rotation rerturbation. In: Proceedings of ICDM, pp. 589–592 (2005)Google Scholar
  15. 15.
    Cheng Q., Huang T.: Robust optimum detection of transform domain multiplicative watermarks. IEEE Trans. Signal Process. 51(4), 906–924 (2003)CrossRefMathSciNetGoogle Scholar
  16. 16.
    Cover, T., Hart, P.: Nearest Neighbor pattern classification. In: IEEE Trans. Inf. Theory, pp. 21–27 (1967)Google Scholar
  17. 17.
    Cox I.J., Kilian J., Leighton T., Shamoon T.: Secure spread spectrum watermarking for multimedia. IEEE Trans. Image Process. 6(12), 1673–1687 (1997)CrossRefGoogle Scholar
  18. 18.
    Cox, I.J., Miller, M.L.: Electronic watermarking: the first 50 years. In: International Conference on Control, Automation, Robotics and Vision (2004)Google Scholar
  19. 19.
    Cox I.J., Miller M.L., Bloom J.A.: Digital watermarking. Morgan Kaufmann, New York (2007)Google Scholar
  20. 20.
    Deshpande, P.M., D.P, Kummamuru, K.: Efficient online top-K retrieval with arbitrary similarity measures. In: Proceedings of EDBT, pp. 356–367 (2008)Google Scholar
  21. 21.
    Fridrich, J.: Minimizing the embedding impact in steganography. In: Proceedings of ACM workshop on Multimedia and security, pp. 2–10 (2006)Google Scholar
  22. 22.
    Fridrich, J., Pevný, T., Kodovský, J.: Statistically undetectable jpeg steganography: dead ends challenges, and opportunities. In: Proceedings of ACM Workshop on Multimedia and security, pp. 3–14 (2007)Google Scholar
  23. 23.
    Green D., Swets J.: Signal detection theory and psychophysics. Wiley, New York (1966)Google Scholar
  24. 24.
    Information Hiding: Techniques for Steganography and Digital Watermarking. Artech House, Boston (2000)Google Scholar
  25. 25.
    Jagannathan, G., Pillaipakkamnatt, K., Wright, R.N.: A new privacy-preserving distributed k-clustering algorithm. In: Proceedings of SIAM International Conference on Data Mining (SDM) (2006)Google Scholar
  26. 26.
    Jin, X., Zhang, Z., Wang, J., Li, D.: Watermarking spatial trajectory database. In: Proceedings of DASFAA (2005)Google Scholar
  27. 27.
    Kargupta, H., Datta, S., Wang, Q., Sivakumar, K.: On the privacy preserving properties of random data perturbation techniques. In: Proceedings of ICDM, pp. 99–106 (2003)Google Scholar
  28. 28.
    Keogh, E., Kasetty, S.: On the need for time series data mining benchmarks: a survey and empirical demonstration. In: Proceedings of SIGKDD, pp. 102–111 (2002)Google Scholar
  29. 29.
    Kesal, M., Mihcak, M.K., Venkatesan, R.: An improved attack analysis on a public-key spread spectrum watermarking. In: ACM Multimedia Systems Journal, pp. 133–142 (2005)Google Scholar
  30. 30.
    Kifer, D., Gehrke, J.: Injecting utility into anonymized datasets. In: Proceedings of SIGMOD, pp. 217–228 (2006)Google Scholar
  31. 31.
    Li, F., Sun, J., Papadimitriou, S., Mihaila, G., Stanoi, I.: Hiding in the crowd: privacy preservation on evolving streams through correlation tracking. In: Proceedings of ICDE, pp. 686–695 (2007)Google Scholar
  32. 32.
    Li S., Okuda M.: Iterative frame decimation and watermarking for human motion animation. Int. J. Graph. Vis. Image Process. 07, 27–34 (2007)Google Scholar
  33. 33.
    Li, T., Li, N.: On the tradeoff between privacy and utility in data publishing. In: Proceedings of SIGKDD, pp. 517–525 (2009)Google Scholar
  34. 34.
    Liu, L., Kantarcioglu, M., Thuraisingham, B.: The applicability of the perturbation model-based privacy preserving data mining for real-world data. In: ICDM International Workshop on Privacy Aspects of Data-Mining (2006)Google Scholar
  35. 35.
    Liu, Y., Prabhakaran, B., Guo, X.: A robust spectral approach for blind watermarking of manifold surfaces. In: Proceedings of ACM Workshop on Multimedia and security, pp. 43–52 (2008)Google Scholar
  36. 36.
    Lucchese, C., Vlachos, M., Rajan, D., Yu, P.: Rights protection of trajectory datasets. In: Proceedings of International Conference on Data Engineering, pp. 1349–1351 (2008)Google Scholar
  37. 37.
    Maity, S.P., Kundu, M.K.: Robust and blind spatial watermarking in digital image. In: Indian Conference on Computer Vision, Graphics and Image Processing (2002)Google Scholar
  38. 38.
    Malvar H., Florencio D.: Improved spread spectrum: a new modulation technique for robust watermarking. IEEE Trans. Signal Process. 51(4), 898–905 (2003)CrossRefMathSciNetGoogle Scholar
  39. 39.
    Moulin, P., Mihcak, M., Lin, G.-I.: An information-theoretic model for image watermarking and data hiding. In: IEEE International Conference on Image Processing (2000)Google Scholar
  40. 40.
    Moulin, P., Mihcak, M.K., Lin, G.I.: An information–theoretic model for watermarking and data hiding. In: Proceedings IEEE International Conference on Image Processing, pp. 667–670 (2000)Google Scholar
  41. 41.
    Niu X., Shao C., Wang X.: A survey of digital vector map watermarking. Int. J. Innov. Comput. Inf. Control 2(6), 1301–1316 (2006)Google Scholar
  42. 42.
    Oliveira, S., Zaiane, O.: Privacy preserving clustering by data transformation. In: Proceedings of SBBD, pp. 304–318 (2003)Google Scholar
  43. 43.
    Perez-Freire L., Perez-Gonzalez F.: Spread-spectrum watermarking security. Inf Forensics Secur. IEEE Trans. 4(1), 2–24 (2009)CrossRefGoogle Scholar
  44. 44.
    Pinkas, B.: Cryptographic techniques for privacy-preserving data mining. In: SIGKDD Explorations 4(2), pp. 12–19 (2002)Google Scholar
  45. 45.
    Rastogi, V., Suciu, D., Hong, S.: The boundary between privacy and utility in data publishing. In: Proceedings of VLDB, pp. 531–542 (2007)Google Scholar
  46. 46.
    Sagetong, P., Zhou, W.: Dynamic wavelet feature-based watermarking for copyright tracking in digital movie distribution systems. In: IEEE International Conference on Image Processing, pp. 653–656 (2002)Google Scholar
  47. 47.
    Simitopoulos, D., Tsaftaris, S., Boulgouris, N., Strintzis, M.: Compressed-domain video watermarking of MPEG streams. In: IEEE International Conference on Multimedia and Expo (ICME) (2002)Google Scholar
  48. 48.
    Sion R., Atallah M., Prabhakar S.: Rights Protection for Relational Data. IEEE Trans. Knowl. Data Eng. 16(12), 1509–1525 (2004)CrossRefGoogle Scholar
  49. 49.
    Sion R., Atallah M.J., Prabhakar S.: Rights Protection for Discrete Numeric Streams. IEEE Trans. Knowl. Data Eng. 18(5), 699–714 (2006)CrossRefGoogle Scholar
  50. 50.
    Solachidis V., Pitas I.: Watermarking polygonal lines using Fourier Descriptors. IEEE Comput. Graph. Appl. 24(3), 44–51 (2004)CrossRefGoogle Scholar
  51. 51.
    Swanson M.D., Zhu B., Tewfik A.H., Boney L.: Robust audio Watermarking Using perceptual masking. Signal Process. 66(3), 337–355 (1998)MATHCrossRefGoogle Scholar
  52. 52.
    Thuraisingham, B.M., Khan, L., Subbiah, G., Alam, A., Kantarcioglu, M.: Privacy and security challenges in GIS. In: Encyclopedia of GIS, pp. 898–902 (2008)Google Scholar
  53. 53.
    Topkara, U., Topkara, M., Atallah, M.J.: The hiding virtues of ambiguity: quantifiably resilient watermarking of natural language text through synonym substitutions. In: MM & Sec, pp. 164–174 (2006)Google Scholar
  54. 54.
    UC Riverside Time Series Data Mining Archive. http://www.cs.ucr.edu/~eamonn/TSDMA/
  55. 55.
    UCI Repository of Machine Learning Databases. http://www.ics.uci.edu/~mlearn/MLRepository.html
  56. 56.
    Voyatzis, G., Pitas, I.: Chaotic mixing of digital images and applications to watermarking. ECMAST 2, pp. 687–694 (1996)Google Scholar
  57. 57.
    Vaidya, J., Clifton, C.: Privacy-preserving K-means clustering over vertically partitioned data. In: SIGKDD (2003)Google Scholar
  58. 58.
    Vaidya, J., Clifton, C.: Privacy preserving naive bayes classifier for vertically partitioned data. In: Proceedings of SDM (2004)Google Scholar
  59. 59.
    Vlachos, M., Lucchese, C., Rajan, D., Yu, P.: Ownership protection of shape datasets with geodesic distance preservation. In: Proceedings of EDBT, pp. 276–286 (2008)Google Scholar
  60. 60.
    Voigt, M., Yang, B., Busch, C.: Reversible watermarking of 2d-vector data. In: Proceedings of the Workshop on Multimedia and Security, pp. 160–165 (2004)Google Scholar
  61. 61.
    Xu, Y., Ke Wang, A.W.-C.F., She, R., Pei, J.: Privacy-preserving data stream classification. In: Advances in Database Systems, pp. 487–510 (2008)Google Scholar
  62. 62.
    Yamazaki, S.: Watermarking motion data. In: Proceedings of Pacific Rim Workshop on Digital Steganography, pp. 177–185 (2004)Google Scholar
  63. 63.
    Yu, H., Jiang, X., Vaidya, J.: Privacy-preserving SVM using nonlinear kernels on horizontally partitioned data. In: SAC, pp. 603–610 (2006)Google Scholar
  64. 64.
    Yu, H., Vaidya, J., Jiang, X.: Privacy-preserving SVM classification on vertically partitioned data. In: Proceedings of PAKDD, pp. 647–656 (2006)Google Scholar
  65. 65.
    Zhu W., Xiong Z., Zhang Y.-Q.: Multiresolution watermarking for images and video. IEEE Trans. Circuits Syst. Video Technol. 9(4), 545–550 (1999)CrossRefGoogle Scholar
  66. 66.
    Zmudzinski, S., Steinebach, M.: Psycho-acoustic model-based message authentication coding for audio data. In: Proceedings of ACM Workshop on Multimedia and security, pp. 75–84 (2008)Google Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Claudio Lucchese
    • 1
  • Michail Vlachos
    • 2
  • Deepak Rajan
    • 3
  • Philip S. Yu
    • 4
  1. 1.I.S.T.I.-C.N.R.PisaItaly
  2. 2.IBM Zürich Research LaboratoryRüschlikonSwitzerland
  3. 3.IBM T.J. Watson Research CenterHawthorneUSA
  4. 4.University of IllinoisChicagoUSA

Personalised recommendations